The Prague Texture Segmentation Datagenerator and Benchmark - Algorithms
optimized
for

The concise description of algorithms contains hyperlinks to further information (author, algorithms details, BIB entry, WWW external page).
The algorithm features are: f1 = classification (supervised segmentation), f2 = hiearchy result (manual selection), f3 = known number of regions, f4 = [reserved].
xaos's
GMRF+EM
version 2.0
BIB
WEB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
M. Haindl & S. Mikes:   Gaussian Markov random field model

An efficient and robust type of unsupervised multispectral texture segmentation method is presented. Single decorrelated monospectral texture factors are assumed to be represented by a set of local Gaussian Markov random field (GMRF) models evaluated for each pixel centered image window and for each spectral band. The segmentation algorithm based on the underlying Gaussian mixture (GM) model operates in the decorrelated GMRF parametric space. The algorithm starts with an oversegmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached.
xaos's
AR3D+EM
version 1.0
BIB
WEB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
M. Haindl & S. Mikes:   3D autoregressive random field model

A new unsupervised multispectral texture segmentation method with unknown number of classes is presented. Multispectral texture mosaics are locally represented by four causal multispectral random field models recursively evaluated for each pixel. The segmentation algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the Prague segmentation benchmark using the commonest segmentation criteria and compares favourably with several alternative texture segmentation methods.
test's
Blobworld
BIB
WEB
f1: 0
f2: 0
f3: 0
f4: 0
Ch. Carson & M. Thomas & S. Belongie & J. M. Hellerstein & J. Malik:   Blobworld: A System for Region-based Image Indexing and Retrieval

Blobworld is a system for content-based image retrieval. By automatically segmenting each image into regions which roughly correspond to objects or parts of objects, we allow users to query for photographs based on the objects they contain.
test's
EDISON
version 1.1
BIB
WEB
f1: 0
f2: 0
f3: 0
f4: 0
Ch.M. Christoudias & B. Georgescu & P. Meer:   Edge Detection and Image SegmentatiON (EDISON) System

This system is a low-level feature extraction tool that integrates confidence based edge detection and mean shift based image segmentation. It was developed by the Robust Image Understanding Laboratory at Rutgers University.
test's
JSEG
BIB
WEB
f1: 0
f2: 0
f3: 0
f4: 0
Y. Deng & B.S. Manjunath:   Unsupervised Segmentation of Color-Texture Regions in Images and Video

A method for unsupervised segmentation of color-texture regions in images and video is presented. This method, which we refer to as JSEG, consists of two independent steps: color quantization and spatial segmentation. In the first step, colors in the image are quantized to several representative classes that can be used to differentiate regions in the image. The image pixels are then replaced by their corresponding color class labels, thus forming a class-map of the image. The focus of this work is on spatial segmentation, where a criterion for ?good? segmentation using the class-map is proposed. Applying the criterion to local windows in the class-map results in the J-image, in which high and low values correspond to possible boundaries and interiors of color-texture regions. A region growing method is then used to segment the image based on the multiscale J-images. A similar approach is applied to video sequences. An additional region tracking scheme is embedded into the region growing process to achieve consistent segmentation and tracking results, even for scenes with nonrigid object motion. Experiments show the robustness of the JSEG algorithm on real images and video.
scarpa's
TFR
BIB
WEB
DOC
f1: 0
f2: 1
f3: 0
f4: 0
G. Scarpa & M. Haindl:   Texture Fragmentation and Reconstruction

The Texture Fragmentation and Reconstruction (TFR) segmentation algorithm is based on a texture modeling particularly suited for segmentation in an unsupervised framework. A texture is regarded for each fixed spatial direction as a finite-state Markov chain where the states of the process are quantized colors. On the basis of this modeling, a simple segmentation algorithm is derived that precesses independently color and spatial information, by first performing a color-based clustering, which provides the quantized colors, and then by means of a further spatial-based clustering, which separates regions according to their transition probability profile. Finally, a region merging algorithm allows to recover the different textures, that is to recompose their internal Markov chains.

scarpa's
TFR/KLD
BIB
WEB
DOC
f1: 0
f2: 1
f3: 0
f4: 0
G. Scarpa & M. Haindl & J. Zerubia:   A Hierarchical Finite-State Model for Texture Segmentation

It is an improved version of the TFR algorithm where the region gain has been changed by introducing a Kullback-Leibler Divergence (KLD) term modeling the region similarity in terms of spatial location.
xaos's
AR3D+EM multi
version 1.0
BIB
WEB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
M. Haindl & S. Mikes:   Multi 3D autoregressive random field model

A novel unsupervised multi-spectral multiple-segmenter texture segmentation method with unknown number of classes is presented. The unsupervised segmenter is based on a combination of several unsupervised segmentation results, each in different resolution, using the sum rule. Multi-spectral texture mosaics are locally represented by four causal multi-spectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the Prague segmentation benchmark using the commonest segmentation criteria and compares favourably with several alternative texture segmentation methods.
test's
EGBIS
BIB
WEB
SRC
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Pedro F. Felzenszwalb and Daniel P. Huttenlocher:   Efficient Graph-Based Image Segmentation

We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.
felipecalderero's
GSRM sup.
version BHAT/KL area-weighted/-unweighted
BIB
WEB
DOC
f1: 0
f2: 0
f3: 1
f4: 0
Felipe Calderero:   General statistical region merging - supervised - 10 bins

General region merging technique based on a size-weighted/-unweighted direct statistical measure of the empirical distributions of the regions, using the Kullback-Leibler divergence/Bhattacharyya coefficient.

This version is supervised, meaning that the number of regions for the evaluated partitions was manually set to the number of regions in the ground truth partitions.

In this implementation, empirical distributions were quantized to 10 bins.
felipecalderero's
GSRM MARKOV sup.
version BHAT/KL area-weighted/-unweighted
WEB
DOC
f1: 0
f2: 0
f3: 1
f4: 0
Felipe Calderero:   General statistical region merging MARKOV - supervised - 10 bins
felipecalderero's
GSRM unsup.
version BHAT/KL area-weighted/-unweighted
BIB
WEB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Felipe Calderero:   General statistical region merging - unsupervised - 10 bins

General region merging technique based on a size-weighted/size-unweighted direct statistical measure of the empirical distributions of the regions, using the Kullback-Leibler divergence/Bhattacharyya coefficient.

This version is UNSUPERVISED, meaning that the number of regions is automatically selected using a significance index.

In this implementation, empirical distributions were quantized to 10 bins.
felipecalderero's
GSRM MARKOV unsup.
version BHAT/KL area-weighted/-unweighted
WEB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Felipe Calderero:   General statistical region merging MARKOV - unsupervised - 10 bins
test's
SWA
version def_par
BIB
WEB
DOC
f1: 0
f2: 1
f3: 0
f4: 0
E. Sharon & M. Galun & D. Sharon & R. Basri & A. Brandt:   SWA algorithm

SWA algorithm segmentation by weighted aggregation, is derived from algebraic multigrid solvers for physical systems, and consists of fine-to-coarse pixel aggregation. Aggregates of various sizes, which may or may not overlap, are revealed as salient, without predetermining their number or scale.
test's
HGS
version E/W/C
BIB
WEB
f1: 0
f2: 0
f3: 0
f4: 0
Minh A. Hoang & Jan-Mark Geusebroek & Arnold W.M. Smeulders:   HGS algorithm

The HGS unsupervised segmenter is based on the integration of the Gabor filters with the measurement of color. Single versions of the method differ in their photometric invariance power (HGS-E no invariance, HGS-W low, HGS-C full invariance). The spatial frequency is measured by sampling the incoming image with a shifted Gaussian in the spatial frequency domain, and the color is measured by sampling the signal with Gaussian in wavelength domain. The method implies that the color?texture is measured in the wavelength-Fourier domain. The measurement filter in this domain boils down to a 3D Gaussian, representing a Gabor?Gaussian in the spatial-color domain.
xaos's
MW3AR
BIB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
M. Haindl, S. Mikeš, P. Pudil:   Hierarchy 3D autoregressive random field model

An unsupervised multi-spectral, multi-resolution, multiple-segmenter for textured images with unknown number of classes is presented. The segmenter is based on a weighted combination of several unsupervised segmentation results, each in different resolution, using the modified sum rule. Multi-spectral textured image mosaics are locally represented by four causal directional multi-spectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached.


test's
test
f1: 0
f2: 0
f3: 1
f4: 0
test's   test method
sylvia's
TEX-ROI-SEG
version 1.0
BIB
WEB
f1: 0
f2: 0
f3: 0
f4: 0
Michael Donoser and Horst Bischof:   Texture ROI-Segmentation

Defaut Parametrization

[1] Donoser, M. and Bischof, H. (2008). Using Covariance Matrices for Unsupervised Texture Segmentation. In Proceedings of International Conference on Pattern Recognition (ICPR) , Tampa, USA.

[2] Donoser, M. and Bischof, H. (2007). ROI-SEG: Unsupervised color segmentation by combining differently focused sub results. In Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA.

Implementation by Sylwia Steginska
xaos's
AR2D+EM
f1: 0
f2: 0
f3: 0
f4: 0
xaos's   AR2D+EM
mrabah's
mine
f1: 1
f2: 1
f3: 1
f4: 0
mrabah's   mine
xaos's
AR3D+EM ii
BIB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
M. Haindl & S. Mikes & P. Vacha:   Illumination Invariant Unsupervised Segmenter

A novel illumination invariant unsupervised multispectral texture segmentation method with unknown number of classes is presented. Multispectral texture mosaics are locally represented by illumination invariants derived from four directional causal multispectral Markovian models recursively evaluated for each pixel. Resulted parametric space is segmented using a Gaussian mixture model based unsupervised segmenter. The segmentation algorithm starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the large illumination invariant benchmark from the Prague Segmentation Benchmark using 21 segmentation criteria and compares favourably with an alternative segmentation method.

sv.pons's
ac-multi
version 1.0
BIB
SRC
DOC
f1: 0
f2: 0
f3: 1
f4: 0
Sandro Vega-Pons; José L. Rodriguez:   Active contour based multiclass texture image segmentation algorithm
jlgil's
Tx-acM
version vers 2
BIB
f1: 0
f2: 0
f3: 1
f4: 0
Vega-Pons, S.; Gil-Rodríguez, J.L. and Vera-Pérez, O.L.:   Active Contourn Algorithm for Texture Segmentation

Tx-acM is an algorithm for unsupervised texture segmentation based on the active contour without edges model with level set representation and a connected component filtering strategy to noise reduce inside each functional minimization step. A set of texture features calculated using several texture models: first order statistic model, co-occurrence matrix model, run length matrix model, Gabor?s descriptors (1D and 2D) and moment?s descriptors are incorporated discreetly in a vector of valued images at the input of the algorithm. See reference [1] for details.
The prefix ?Tx? makes reference to ?Tx Estudio?, a system oriented to textured image segmentation making use of the paradigms: tone, texture and/or tone+texture. Several non-supervised image segmentation algorithms are implemented into the system: Tx-acM, Tx-acB, Tx-kMeans, Tx-fuzzykMeans,Tx-MeanShift, Tx-ART2, Tx-fuzzyART and Tx-SOM.

[1] Vega-Pons, S.; Gil-Rodríguez, J.L and Vera-Pérez, O.L. (2008). ?Active contour algorithm for texture segmentation using a texture feature set?. In 19th International Conference on Pattern Recognition. ICPR2008. IEEE Computer Society. TuBCT8.32, (1-4), 2008. ISSN: 1051-4651, ISBN: 978-1-4244-2174-9. DOI:10.1109/ICPR.2008.4761583

mahelpet's
noname
f1: 1
f2: 0
f3: 0
f4: 0
mahelpet's   noname
hadacmar's
CoOccurenceFeatures
version 1
SRC
f1: 1
f2: 0
f3: 1
f4: 0
Martin Hadacek:   CoOccurenceFeatures

CoOccurence features and Nearest neighbour is used.
leskajur's
LFM + kNN
SRC
f1: 1
f2: 0
f3: 1
f4: 0
Juraj Leškanič:   Laws filter masks with k-NN classifier

LFM used for feature extraction
kNN used for classification
borkojos's
cooccurrence&naive bayes
SRC
f1: 1
f2: 0
f3: 1
f4: 0
borkojos's   cooccurrence&naive bayes
ales.hejl's
ed_ah
version 1
f1: 0
f2: 0
f3: 0
f4: 0
ales.hejl's   ed_ah
jirkujak's
LBP
version 3,1
f1: 1
f2: 0
f3: 1
f4: 0
Mäenpää, T. and Pietikäinen, M:   Local Binary Patterns

See http://www.scholarpedia.org/article/Local_Binary_Patterns
yzan's
Laws & Naive Bayes
SRC
f1: 1
f2: 0
f3: 1
f4: 0
yzan's   Laws & Naive Bayes

For feature extraction Laws' texture filter was used. The features were then classified with Naive Bayes.
luptarad's
laws masks & kNN
version 1.0
SRC
f1: 1
f2: 0
f3: 1
f4: 0
Luptak:   segmentation using laws masks and filter
munozfab's
MRF
version Gibbs sampler
BIB
WEB
f1: 1
f2: 0
f3: 1
f4: 0
Kato, Zoltan and Pong, Ting-Chuen and Lee, John Chung-Mong:   Color image segmentation and parameter estimation in a markovian framework

A color image segmentation algorithm uses a Markov random field (MRF) pixel classification model. The method estimates initial mean vectors effectively even if the histogram does not have clearly distinguishable peaks. The parameter supplied by the user is the number of classes and rectangles over representative regions of the classes.
feantury's
KMeans
version 1.0
SRC
f1: 1
f2: 0
f3: 1
f4: 0
feantury's   KMeans
ovesnmar's
UPGMA+kNN
version 1
WEB
SRC
f1: 1
f2: 0
f3: 0
f4: 0
Martin Ovesný:   Agglomerative clustering + k Nearest Neighbours

UPGMA -> feature extraction
kNN -> classification
Component's boundary histograms -> post-processing

dvoromar's
AM + kNN
f1: 1
f2: 0
f3: 0
f4: 0
dvoromar's   Arithmetic mean + k nearest neighbours

Without postprocessing.
zamecdus's
Laws+kNN
SRC
f1: 1
f2: 0
f3: 0
f4: 0
Dušan Zámečník:   Laws+kNN

Laws Masks Filtering with kNN classifier.
O.Kucera's
LBP + kNN
WEB
f1: 1
f2: 0
f3: 1
f4: 0
O.Kucera's   Local binary patterns
medonvoj's
rozpoznavani
f1: 0
f2: 0
f3: 0
f4: 0
medonvoj's   rozpoznavani
SKE's
Neuralnet
f1: 1
f2: 0
f3: 1
f4: 0
SKE's   Neuralnet
fabiaja's
1NN
version 1
SRC
f1: 1
f2: 0
f3: 0
f4: 0
fabiaja's   Nearest neighbour
pistekjakub's
Coocurence matrix properties with Bayess Classificator
version 1.0
WEB
SRC
DOC
f1: 1
f2: 0
f3: 0
f4: 0
Jakub Pištěk:   Textural Features for Image Classification - Coocurence matrix properties with Bayess Classificator (ROBERT M. HARALICK, K. SHANMUGAM, AND ITS'HAK DINSTEIN)

Použil jsem jednotlivé vlasnosti koonkurenční matice k tomu abych naušil bayessův klasifikátor z trénovacích dat. Trénování jsem použil vždy sumu každé vlastnosti přes všechny spektra a to pro každý typ koonkurenční matice zvlášť.
kupcimat's
Co-occurrence matrix + Naive Bayes
version 1.0
SRC
f1: 1
f2: 0
f3: 0
f4: 0
Matej Kupčiha:   Co-occurrence matrix + Naive Bayes

Image segmentation project including feature extraction (co-occurrence matrix) and classification (Naive Bayes) and some post-process filtering.
connetwork12's
conn-gl-noblur
f1: 1
f2: 0
f3: 0
f4: 0
connetwork12's   conn-gl-noblur
connetwork12's
conn-col-blur6
f1: 1
f2: 0
f3: 0
f4: 0
connetwork12's   conn-col-blur6
connetwork12's
conn-col-blur3
f1: 1
f2: 0
f3: 0
f4: 0
connetwork12's   conn-col-blur3
connetwork12's
conn-col-noblur
f1: 1
f2: 0
f3: 0
f4: 0
connetwork12's   conn-col-noblur
scarpa's
R-TFR/M
version 1
BIB
DOC
f1: 0
f2: 1
f3: 0
f4: 0
R. Gaetano, G. Scarpa, G. Poggi:   Recursive TFR (with manual selection)

The Texture Fragmentation and Reconstruction (TFR) algorithm, recently proposed for the segmentation of textured images, has been applied with promising results to high-resolution remote-sensing images. The algorithm provides a sequence of nested segmentation maps which allow the analysis at various scales of observation. However, the performance which is very good at large scales, with complex semantic areas retrieved with remarkable accuracy, becomes less satisfactory at finer scales. By using the TFR in a recursive fashion, segmenting the image in just two regions, initially, with each region further segmented only if relevant subregions emerge, we get the Recursive TFR (R-TFR). R-TFR allows one to better adapt to local statistics and to extract significant textures also at finer scales. In this version the best segmentation scale is manually selected.
lingxiu's
ICM
version matlab
SRC
f1: 1
f2: 0
f3: 0
f4: 0
lingxiu's   ICM
rozhddmi's
firstTry
version 0001
SRC
f1: 1
f2: 0
f3: 0
f4: 0
Rozhdestvenskiy Dmitry:   firstTry

MAthlab classification example
training set 10x10 pixels from all training samples
rozhddmi's
Bayes classifaer
version 1.0
SRC
f1: 1
f2: 0
f3: 0
f4: 0
rozhddmi's   Bayes classifaer

Using full multivariate distribution function and bayes theorem for classification, 4 cycle classification with updating prior (row / column row+column)
lecalthi's
Matlab classification
SRC
f1: 1
f2: 0
f3: 0
f4: 0
lecalthi's   Matlab classification

Pattern recognition using probability
rozhddmi's

version 1.1
SRC
f1: 1
f2: 0
f3: 1
f4: 0
Rozhdestvenskiy Dmitry:   BAYES CLASSIFIER NAIVE AND LOSSLES

Naive and lossless classifier with updating prior,
renata.rieger's
Matlab quadratic
version 1.1
SRC
f1: 1
f2: 0
f3: 0
f4: 0
renata.rieger's   Matlab quadratic
rozhddmi's
Class_upate
version 1.0
SRC
f1: 1
f2: 0
f3: 1
f4: 0
rozhddmi's   Class_upate

Updated version of the previous now class labels are chosen according data.xml
Joaneta's
ColorHist
version 2.0
f1: 1
f2: 0
f3: 0
f4: 0
Joaneta's   ColorHist
Joaneta's
ColorHist2
version 3.1
f1: 1
f2: 0
f3: 1
f4: 0
Joaneta's   ColorHist2
josuegalindo's
Quad
version 1.0
f1: 1
f2: 0
f3: 1
f4: 0
Jg:   Quad texture

segments 4 txtures
josuegalindo's
superRegionknown
f1: 1
f2: 0
f3: 1
f4: 0
josuegalindo's   superRegionknown
toruming's
f1: 0
f2: 0
f3: 0
f4: 0
toruming's   
david0432's
Cooperative Mum-Shah
version 1.1
f1: 0
f2: 0
f3: 1
f4: 0
David Perez, Felipe Calderero:   Cooperative Region Merging
david0432's
Cooperative Mum-Shah Weighted Memory
f1: 0
f2: 0
f3: 1
f4: 0
David Perez, Felipe Calderero:   Cooperative Mum-Shah Weighted Memory
david0432's
Cooperative Mum-Shah Simple Memory
f1: 0
f2: 0
f3: 1
f4: 0
david0432's   Cooperative Mum-Shah Simple Memory
rohit.iiith's
try
f1: 0
f2: 0
f3: 0
f4: 0
rohit.iiith's   try
rohit.iiith's
LBP based texture segmentation
f1: 0
f2: 0
f3: 0
f4: 0
rohit.iiith's   LBP based texture segmentation
rohit.iiith's
Graph based Segmentation
f1: 0
f2: 0
f3: 0
f4: 0
rohit.iiith's   Graph based Segmentation
ldof's
Automatic Dynamic Texture Segmentation Using Local Descriptors
SRC
f1: 0
f2: 0
f3: 0
f4: 0
ldof's   Automatic Dynamic Texture Segmentation Using Local Descriptors
SKEeks's
KNN
version v1
f1: 1
f2: 0
f3: 0
f4: 0
SKEeks's   KNN
SKEeks's
KNN
version v2
f1: 1
f2: 0
f3: 1
f4: 0
SKEeks's   KNN
celine.chinoise's
sas_gmm(color+tensor)
f1: 0
f2: 0
f3: 1
f4: 0
celine.chinoise's   sas_gmm(color+tensor)
celine.chinoise's
sas_gmm(color)
version c=7
f1: 0
f2: 0
f3: 1
f4: 0
celine.chinoise's   sas_gmm(color)
celine.chinoise's
gaussien
f1: 0
f2: 0
f3: 1
f4: 0
celine.chinoise's   gaussien

CVPR: segmentation using supperpixels: a bipartite graph partitioning approach
celine.chinoise's
weighted_color_patch
f1: 0
f2: 0
f3: 0
f4: 0
celine.chinoise's   xiaofang wang
celine.chinoise's
mm_s
f1: 0
f2: 0
f3: 0
f4: 0
celine.chinoise's   mm_s
celine.chinoise's
mm_s
f1: 0
f2: 0
f3: 0
f4: 0
celine.chinoise's   mm_s
celine.chinoise's
gmm_sift
version c=7
f1: 0
f2: 0
f3: 0
f4: 0
xiaofang wang:   gmm_sift
celine.chinoise's
sas
f1: 0
f2: 0
f3: 0
f4: 0
celine.chinoise's   sas
celine.chinoise's
gmm_lrr
f1: 0
f2: 0
f3: 1
f4: 0
celine.chinoise's   gmm_lrr
celine.chinoise's
sas_gmm_withoutSparseCoding
f1: 0
f2: 0
f3: 1
f4: 0
celine.chinoise's   sas_gmm_withoutSparseCoding
celine.chinoise's
SR_multifeat
version all
f1: 0
f2: 0
f3: 1
f4: 0
celine.chinoise's   SR_multifeat
xaos's
LBP + EM
f1: 0
f2: 0
f3: 0
f4: 0
xaos's   LBP + EM
FIT2012's
HCSS
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Michael Donoser, Martin Urschler, Hayko Riemenschneider and Horst Bischof:   Highly Consistent Sequential Segmentation

An attempt to implement HCSS with some features missing and some features added.
chaththa85's
ImprvGMRF
version 2
f1: 0
f2: 0
f3: 1
f4: 0
C. Dharmagunawardhana, S. Mahmoodi, M. Niranjan, M. Bennett:   Improved Gaussian Markov Random Field Texture Features

An improved semi parametric method of texture feature formulation using GMRFs.
akshaya's
Bayesian&near neighbour
version 2
WEB
SRC
DOC
f1: 1
f2: 0
f3: 0
f4: 0
Sylvain:   auto classification
matowmic's
SRC
f1: 1
f2: 0
f3: 0
f4: 0
matowmic's   

Segmentation source for greyscale found on forum of matlab community.
icpr2014_test's
icpr2014_alg
f1: 0
f2: 0
f3: 0
f4: 0
icpr2014_test's   icpr2014_alg
frzn's
IUT_texNCUT
f1: 0
f2: 0
f3: 1
f4: 0
Farzaneh Alizadeh, Nader Karimi, Niloofar Gheissari:   Textural image segmentation using Normalized Cut

TexNCUT use from Texture features and a graph based image segmentation method(Ncut) for textural image segmentation. our algorithm employ super-pixels to increase speed and efficiency.

In TexNCUT the number of regions for the evaluated partitions was manually set to the number of regions in the ground truth partitions.
Karimi's
test 1
f1: 0
f2: 0
f3: 1
f4: 0
Karimi's   test 1
polkennel's
DTCWT_texton_SVM
f1: 1
f2: 0
f3: 0
f4: 0
polkennel's   DTCWT_texton_SVM
scarpa's
R-TFR/K
version 1
BIB
DOC
f1: 0
f2: 0
f3: 1
f4: 0
R. Gaetano, G. Scarpa, G. Poggi:   R-TFR (with known number of classes)

The Texture Fragmentation and Reconstruction (TFR) algorithm, recently proposed for the segmentation of textured images, has been applied with promising results to high-resolution remote-sensing images. The algorithm provides a sequence of nested segmentation maps which allow the analysis at various scales of observation. However, the performance which is very good at large scales, with complex semantic areas retrieved with remarkable accuracy, becomes less satisfactory at finer scales. By using the TFR in a recursive fashion, segmenting the image in just two regions, initially, with each region further segmented only if relevant subregions emerge, we get the Recursive TFR (R-TFR). R-TFR allows one to better adapt to local statistics and to extract significant textures also at finer scales. In this version the number of classes is known a priory.
scarpa's
TS-MRF/M
version 1
BIB
DOC
f1: 0
f2: 1
f3: 0
f4: 0
C. D'Elia, G. Poggi, G.Scarpa:   Tree-structured Markov Random Field (with manual selection)

The Tree-Structured Markov Random Field (TS-MRF) algorithm is a spectral-based (hence not texture-based) classifier which utilizes MRF prior models to get regularized segmentations.
The image is recursively segmented in smaller and smaller regions until a stopping condition, local to each region, is met. Each elementary binary segmentation is obtained as the solution of a MAP estimation problem, with the region prior modeled as an MRF. Since only binary fields are used, and thanks to the tree structure, the algorithm is quite fast, and allows one to address the cluster validation problem in a seamless way. In addition, all field parameters are estimated locally, allowing for some spatial adaptivity.
In this version the proper segmentation scale (tree pruning) is left to the user.
scarpa's
TS-MRF/K
version 1
BIB
DOC
f1: 0
f2: 0
f3: 1
f4: 0
C. D'Elia, G. Poggi, G.Scarpa:   Tree-structured Markov Random Field (with known number of classes)

The Tree-Structured Markov Random Field (TS-MRF) algorithm is a spectral-based (hence not texture-based) classifier which utilizes MRF prior models to get regularized segmentations.
The image is recursively segmented in smaller and smaller regions until a stopping condition, local to each region, is met. Each elementary binary segmentation is obtained as the solution of a MAP estimation problem, with the region prior modeled as an MRF. Since only binary fields are used, and thanks to the tree structure, the algorithm is quite fast, and allows one to address the cluster validation problem in a seamless way. In addition, all field parameters are estimated locally, allowing for some spatial adaptivity.
In this version the number of classes is an input parameter.
scarpa's
DHC/M
version 1
BIB
DOC
f1: 0
f2: 1
f3: 0
f4: 0
G. Scarpa, G. Masi, R. Gaetano, L. Verdoliva, G. Poggi:   Dynamic Hierarchical Classifier (with manual selection)

Recursive tree-structured segmentation is a powerful tool to deal with the non-stationary nature of images. By fitting model parameters to each region/class under analysis one can adapt the segmentation algorithm to the local image statistics, thus improving accuracy. However, a single model/segmenter cannot fit regions with wildly different nature, and one should be allowed to select in a suitable library the tool most suited to the local statistics. The dynamic segmentation/classification algorithm (DHC), uses two segmenters, based on spectral and textural properties, respectively, and a suitable rule for switching model locally.
In this version the segmentation is hand-picked from the hierarchical segmentation stack.
scarpa's
DHC/K
version 1
BIB
DOC
f1: 0
f2: 0
f3: 1
f4: 0
G. Scarpa, G. Masi, R. Gaetano, L. Verdoliva, G. Poggi:   Dynamic Hierarchical Classifier (with known number of classes)

Recursive tree-structured segmentation is a powerful tool to deal with the non-stationary nature of images. By fitting model parameters to each region/class under analysis one can adapt the segmentation algorithm to the local image statistics, thus improving accuracy. However, a single model/segmenter cannot fit regions with wildly different nature, and one should be allowed to select in a suitable library the tool most suited to the local statistics. The dynamic segmentation/classification algorithm (DHC), uses two segmenters, based on spectral and textural properties, respectively, and a suitable rule for switching model locally.
In this version the number of classes is given as input.
cpanag@csd.uoc.gr's
Results_vote_Class_merge
WEB
f1: 0
f2: 0
f3: 0
f4: 0
Costas Panagiotakis, Ilias Grinias and Georgios Tziritas:   Texture Segmentation Based on Voting of Blocks, Bayesian Flooding and Region Merging

We propose an unsupervised texture image
segmentation framework with unknown number of regions,
which involves feature extraction and classification in feature
space, followed by flooding and merging in spatial domain.
The distribution of the features for the different classes are
obtained by a block-wise unsupervised voting framework
using the blocks grid graph or its minimum spanning tree
and the Mallows distance. The final clustering is obtained
by using the k-centroids algorithm. An efficient flooding
algorithm is used, namely, Priority Multi-Class Flooding Algorithm (PMCFA), that assign pixels to labels using Bayesian
dissimilarity criteria. Finally, a region merging method,
which incorporates boundary information, is introduced for
obtaining the final segmentation map. The proposed scheme
is executed for several number of regions, we select the
number of regions that minimize a criterion that takes into
account the average likelihood per pixel of the classification
map and penalizes the complexity of the regions boundaries.
Segmentation results on the Prague benchmark data set
demonstrate the high performance of the proposed scheme.
MikeDoni's
ICG_Segmenter
version 1.0
f1: 0
f2: 0
f3: 0
f4: 0
Michael Donoser:   ICG_Segmenter
cpanag@csd.uoc.gr's
Results_kmeans_Segm
version 1.0
f1: 0
f2: 0
f3: 0
f4: 0
Costas Panagiotakis, Ilias Grinias and Georgios Tziritas:   Results_kmeans_Segm
cpanag@csd.uoc.gr's
Results_kmeans_Segm KnownNumber of Clusters
version 1.0
f1: 0
f2: 0
f3: 1
f4: 0
Costas Panagiotakis, Ilias Grinias and Georgios Tziritas:   Results_kmeans_Segm KnownNumber of Clusters
jolen217's
deep_brain_model
version 1.0
BIB
WEB
f1: 0
f2: 0
f3: 0
f4: 0
Nan Zhao:   Deep Brain Model

Deep brain model is an unsupervised segmentation framework with unknown number of classes simulating the deep structure of the primate visual cortex. This model is based on a deep scale space in which a pool of receptive field models in pre-cortical processing and early vision is applied in each scale to produce feature maps. The graph-based image segmentation is then employed to select object boundaries among the edges of superpixels.
py's
CGCHi
version 0.0.1
f1: 0
f2: 0
f3: 0
f4: 0
Ruhallah Amandi, Mohammad Farhadi:   Combined Graph Cut based segmentation with histogram information on regions

Segmentation is one the more challenging problems in image processing. All segmentation algorithms must answer the three main questions, coherent definition-method to encode coherent in mathematical notion and give the final solution for such system. In this work we use the combination of global and local coherent. Find the sufficient number of clusters do by using histograms and probability theory, on the next part we use the metric space strategies to model local intensity feature of input image. Some problems in this method are from two main sources, wrong number of cluster estimation and the other one is the modeling method failures.



Multiregion Image Segmentation by Parametric Kernel Graph Cuts

Histogram clustering for unsupervised image segmentation

hnizdja2's
LevelSet
version 1,0
BIB
SRC
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Jan Hnízdil, Šimon Hlaváč, Tomáš Kuzin:   MI-ROZ LevelSetSegmentation

School work on FIT ČVUT.
Implementation of pattern recognition algorithm based on IEEE article:

Level Set Segmentation With Multiple Regions
written by Thomas Brox and Joachim Weickert
perutond's
mean-shift
version 1.0
BIB
SRC
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Ondřej Perutka, David Vaníček:   mean-shift

Image segmentation based on the mean-shift algorithm.
palkoigo's
S-SRM
version 0.5
BIB
SRC
f1: 0
f2: 0
f3: 1
f4: 0
Igor Palkoci:   Statistical Region Merging - Supervised Version
palkoigo's
U-SRM
version 0.5
BIB
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Igor Palkoci:   Statistical Region Merging - Unsupervised Version
hejlfran's
CTEX
version 1.0
f1: 0
f2: 0
f3: 0
f4: 0
Dana E. Ilea and Paul F. Whelan:   An Adaptive Unsupervised Segmentation Algorithm Based on Color-Texture Coherence
bartejak's
Energy Minimization with Label Costs
version 1.0
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Jakub Bartel, Libor Jonát, Pavel Zbytovský:   Energy Minimization with Label Costs

Implementace algoritmu podle článku Fast Approximate Energy Minimization with Label
Costs
ebasaeed's
WS
version Paper (mscnn; boosting; mean, no Merging) v27
f1: 1
f2: 1
f3: 0
f4: 0
ebasaeed's   WS
karimaig@fd.cvut.cz's
f1: 1
f2: 0
f3: 0
f4: 0
karimaig@fd.cvut.cz's   
karimaig@fd.cvut.cz's
f1: 1
f2: 0
f3: 0
f4: 0
karimaig@fd.cvut.cz's   
mashkali's
mashkali image segmentation
SRC
f1: 1
f2: 0
f3: 0
f4: 0
mashkali's   mashkali image segmentation
yuanj's
FSEG
version 1.0
WEB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Jiangye Yuan:   FSEG

The factorization based texture segmentation algorithm is applied.

No human interaction or prior information is needed.
dteney's
test
f1: 0
f2: 0
f3: 1
f4: 0
dteney's   test
xaos's
AR3D+EM dyn
BIB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Haindl Michal, Mikeš Stanislav:   Unsupervised Dynamic Textures Segmentation

An unsupervised dynamic colour texture segmentation method uses unknown and variable number of texture classes. Single regions with dynamic textures can furthermore dynamically change their location as well as their shape. Individual dynamic multispectral texture mosaic frames are locally represented by Markovian features derived from four directional multispectral Markovian models recursively evaluated for each pixel site. Estimated frame-based Markovian parametric spaces are segmented using an unsupervised segmenter derived from the Gaussian mixture model data representation which exploits contextual information from previous video frames segmentation history. The segmentation algorithm for every frame starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached.
feoktistov's
test
f1: 0
f2: 0
f3: 0
f4: 0
feoktistov's   test
scarpa's
eCog
BIB
DOC
f1: 0
f2: 1
f3: 0
f4: 0
Martin Baatz and Arno Schäpe:   eCognition

From abstract:
The approach [...] aims for an universal high-quality solution applicable and adaptable to many problems and data types. As each image analysis problem deals with structures of a certain spatial scale, the average image objects size must be free adaptable to the scale of interest.
This is achieved by a general segmentation algorithm based on homogeneity definitions in combination with local and global optimization techniques. A scale parameter is used to control the average image object size. [...]
scarpa's
ENVI
BIB
DOC
f1: 0
f2: 1
f3: 0
f4: 0
Jin Xiaoying:   ENVI

A digital image can be processed by an image processing method that calculates a gradient map for the digital image, calculates a density function for the gradient map, calculates a modified gradient map using the gradient map, the density function and the selected scale level, and segments the modified gradient map. Prior to segmenting the modified gradient map, a sub-image of the digital image can be segmented at the selected scale level to determine if the selected scale level will give the desired segmentation.
Alojz's
TAlgorithm
version 0.1
SRC
f1: 0
f2: 0
f3: 1
f4: 0
T. Tethal:   TTAlgorithm

K-means in Lab feature space
LiRa's
f1: 0
f2: 0
f3: 0
f4: 0
LiRa's   
MozG's
f1: 1
f2: 0
f3: 0
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MozG's   
MozG's
Seg
f1: 1
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f3: 0
f4: 0
MozG's   Seg
matuslorinc's
SRC
f1: 0
f2: 0
f3: 0
f4: 0
matuslorinc's   
yuanj's
FSEG_woMg
f1: 0
f2: 0
f3: 0
f4: 1
yuanj's   FSEG_woMg
horkyja6's
FITsegmenter
version 1.2
WEB
SRC
f1: 0
f2: 0
f3: 0
f4: 0
horkyja6's   FITsegmenter
horkyja6's
FITsegmenterTEST
version 1.2
SRC
f1: 0
f2: 0
f3: 0
f4: 0
horkyja6's   FITsegmenterTEST
garmanik's
algor2
version 2
f1: 1
f2: 0
f3: 0
f4: 0
yuan:   factorization easytexture

J. Yuan and D. L. Wang. Factorization-based texture segmentation. Technical Report OSU-CISRC-1/13 -TR01, 2013.
iaksppo's
factor3
version 3
SRC
f1: 1
f2: 0
f3: 0
f4: 0
same:   factorization laplacian or gaussian + circular averaging filters

ssa
iaksppo's
grayscale factor
version 3
f1: 1
f2: 0
f3: 0
f4: 0
niko:   graysc

ssd
koppmaty's
TEST
version 0.00
SRC
f1: 0
f2: 0
f3: 0
f4: 0
koppmaty's   TEST
martinkersner's
Genetic Algorithm
version 1.0.
f1: 0
f2: 0
f3: 0
f4: 0
martinkersner's   Genetic Algorithm

multi-thresholding
Mori's
Morfological Opening Filter
version 1
f1: 0
f2: 0
f3: 0
f4: 0
Veronika Maurerova:   Morfological Opening Filter

Morphological opening on an image is defined as an erosion followed by a dilation. Opening can remove small bright spots (i.e. “salt”) and connect small dark cracks.

(source: http://scikit-image.org/docs/dev/auto_examples/applications/plot_morphology.html)
ondrasim's
test
version test
SRC
f1: 0
f2: 0
f3: 0
f4: 0
ondrasim's   test

test
richtto6's
Histogram ratio features
version 0.1
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Tomas Richtr:   Histogram ratio features for color texture classification

Implementation of classification by article Histogram ratio features for color texture classification for MI-ROZ.
palicand's
voting max
version 0.1
f1: 0
f2: 0
f3: 1
f4: 0
palicand's   Voting Maximization

Something
koppmaty's
convolution
version 0.05
f1: 0
f2: 0
f3: 0
f4: 0
koppmaty's   convolution
horkyja6's
FITsegmenter
version 1.3 core
WEB
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Stanislav Mikeš:   FITsegmenter
koppmaty's
CONVOLUTION
version 1.17
SRC
f1: 0
f2: 0
f3: 0
f4: 0
koppmaty's   CONVOLUTION
sabattom's
NCuts
version 1.0.0
SRC
DOC
f1: 0
f2: 0
f3: 1
f4: 0
Bc. Krákora Vojtěch, Bc. Šabata Tomáš:   Normalized Cuts

Normalized Cuts and Image Segmentation
Jianbo Shi and Jitendra Malik, Member, IEEE
richtto6's
Histogram ratio features
version 1.0
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Tomas Richtr:   Histogram ratio features for color texture classification

Implementation of classification by article Histogram ratio features for color texture classification for MI-ROZ.
dlapavoj's
ROZ_LBP
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Vojtech Dlapal:   Local binary patterns by Vojtech Dlapal
ruhiravichandran's

version 1.1
SRC
f1: 0
f2: 0
f3: 0
f4: 0
ruhiravichandran's   
barusmar's
wold
version 1
SRC
f1: 0
f2: 0
f3: 0
f4: 0
fit cvut, martin barus:   wold decomposition

school project
koppmaty's
CONVOLUTION
version 2.17
SRC
f1: 0
f2: 0
f3: 0
f4: 0
koppmaty's   CONVOLUTION
bilikdav's
ROZ-bilikdav
version 1
f1: 1
f2: 0
f3: 1
f4: 0
bilikdav's   ROZ-bilikdav
zitnyjak's
calderero_kl_weighted
version 1.0
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Tomas Makara, Jakub Zitny, Lukas Polak:   KL-Weighted Segmentation

Segmentation algorithm based on paper "Region Merging Techniques Using Information Theory Statistical Measures" by Calderero and Marques - area weighted with Kullback-Leibler merging criterion. Binary is 64bit.
zitnyjak's
calderero_kl_unweighted
version 1.0
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Tomas Makara, Jakub Zitny, Lukas Polak:   calderero_kl_unweighted

Segmentation algorithm based on paper "Region Merging Techniques Using Information Theory Statistical Measures" by Calderero and Marques - area unweighted with Kullback-Leibler merging criterion. Binary is 64bit.
zitnyjak's
calderero_bhat_weighted
version 1.0
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Tomas Makara, Jakub Zitny, Lukas Polak:   calderero_bhat_weighted

Segmentation algorithm based on paper "Region Merging Techniques Using Information Theory Statistical Measures" by Calderero and Marques - area weighted with Bhattacharyya merging criterion. Binary is 64bit.
zitnyjak's
calderero_bhat_unweighted
version 1.0
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Tomas Makara, Jakub Zitny, Lukas Polak:   calderero_bhat_unweighted

Segmentation algorithm based on paper "Region Merging Techniques Using Information Theory Statistical Measures" by Calderero and Marques - area unweighted with Bhattacharyya merging criterion. Binary is 64bit.
labanjak's
Affinity Propagation
version 1.1.0
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Jakub Labant, Andrej Kudinov, Dausheyev Jorj-Mukhammed:   Clustering by passing messages between data points
stameser's
Autocorrelation function
version 0.9
SRC
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Petrou:   Autocorrelation function

auto-correlation function looks in image as on random field and calculates how it's correlated with itself.
More details in book (see link below). Page 196.
malyja's
scale_aap_segm
version 1.0
f1: 0
f2: 0
f3: 0
f4: 0
Jan Malý @ malyja16@fit.cvut.cz:   The Scale of a Texture and its Application to Segmentation
frydatom's
Object boundary location by region and contour deformation
version 1
SRC
DOC
f1: 0
f2: 0
f3: 1
f4: 0
Tomáš Frýda:   Object boundary location by region and contour deformation

Object boundary location by region and contour deformation

Postprocessing
horkyja6's
FITsegmenter 1.3
version v8x
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Jan Horký:   FITsegmenter 1.3
guloleg's
HOG
version 1
SRC
f1: 1
f2: 0
f3: 1
f4: 0
guloleg's   HOG
kubeljit's
Run len matrix
version 1.0
f1: 0
f2: 0
f3: 0
f4: 0
kubeljit's   Run len matrix
horkyja6's
FITsegmenter 1.3
version v10x
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Jan Horký:   FITsegmenter 1.3
horkyja6's
FITsegmenter 1.3
version v11x
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Jan Horký:   FITsegmenter 1.3
kubeljit's
Run len matrix
version 2.0
SRC
f1: 0
f2: 0
f3: 0
f4: 0
kubeljit's   Run len matrix

Přidány všechny features popsané v článku - Galloway, Chu a Dasarathy&Holde.
polakluk's
MI-ROZ sem
f1: 0
f2: 0
f3: 1
f4: 0
Zitny, Polak, Makara:   MI-ROZ sem
hakmart1's
LevelSet
version 2
f1: 0
f2: 0
f3: 0
f4: 0
Martin Hak, Jiří Kovačič, Vojtěch Stránský:   LevelSet segmenter

Project for ROZ. Implementation of segmenter based on levelset method.
nenenevg's
LBP
version 13
WEB
SRC
f1: 0
f2: 0
f3: 0
f4: 0
Ojala et al. 1996:   Local binary patterns

Local binary patterns (LBP) is a type of feature used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990. Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. Due to its discriminative power and computational simplicity, LBP texture operator has become a popular approach in various applications. It can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis.
xiaofang's
LocalGlobalGraph
version color
f1: 0
f2: 1
f3: 0
f4: 0
Xiaofang Wang:   LocalGlobalGraph
yuanj's
RS
f1: 0
f2: 0
f3: 0
f4: 1
yuanj's   RS
cbampis's
Test
f1: 0
f2: 0
f3: 0
f4: 0
Christos Bampis:   Test
daicav's
SegTexCol
version 1
SRC
f1: 0
f2: 0
f3: 0
f4: 0
daicav's   Segmentation using texture and colour information
xaos's
MW3AR8^i
BIB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Michal Haindl, Stanislav Mikes, and Mineichi Kudo:   Unsupervised Surface Reflectance Field Multi-Segmenter

An unsupervised, illumination invariant, multi-spectral, multi-resolution, multiple-segmenter for textured images with unknown number of classes is presented. The segmenter is based on a weighted combination of several unsupervised segmentation results, each in different resolution, using the modified sum rule. Multi-spectral textured image mosaics are locally represented by eight causal directional multi-spectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the Prague segmentation benchmark using the commonest segmentation criteria and compares favourably with several leading alternative image segmentation methods.
novotm@fit.cvut's
Gabor features
SRC
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novotm@fit.cvut's   Gabor features
kuzelon3's
LBP
version 1.0
SRC
f1: 0
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f4: 0
Ondřej Kužela:   Local Binary Patterns

MI-ROZ course project
veselj38's
Histogram ratio features
version 0.1
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veselj38's   Histogram ratio features
haurvojt's
grayscale
version 1.0
SRC
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haurvojt:   color image calculated to grayscale features
friedmag's
mi-roz
version 0.1
SRC
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friedmag's   CBP
tomas.duda's
MI-ROZ
version 0.1
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Tomáš Duda:   Rozpoznávání - test

Test runs.
ondrasim's
FIT_ROZ_15
version v_0.0.1
SRC
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ondrasim's   FIT_ROZ_15

gray
ondrasim's
FIT_ROZ_15
version v_1.0.0
SRC
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Šimon Ondráček:   FIT_ROZ_15

Local difference
prochm35's
Dominant Neighborhood Structure
version 12
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prochm35's   Dominant Neighborhood Structure
svehlja5's
Luminance
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svehlja5's   Luminance
svehlja5's
Histograms of Oriented Gradients
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svehlja5's   Histograms of Oriented Gradients
test's
TBES
version 1.0
WEB
DOC
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Shankar Rao, Hossein Mobahi, Allen Yang, Shankar Sastry and Yi Ma:   Natural Image Segmentation with Adaptive Texture and Boundary Encoding

Algorithm for unsupervised segmentation of natural images that harnesses the principle of minimum description length (MDL). The method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by an adaptive chain code. The optimal segmentation of an image is the one that gives the shortest coding length for encoding all textures and boundaries in the image, and is obtained via an agglomerative clustering process applied to a hierarchy of decreasing window sizes. The optimal segmentation also provides an accurate estimate of the overall coding length and hence the true entropy of the image.
kukacji1's
ASATCSI
version 1.1
BIB
DOC
f1: 1
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f3: 1
f4: 0
Manik Varma, Andrew Zisserman:   A Statistical Approach to Texture Classification from Single Images

This algorithm classifies textures based on known database of textures (also generated in this agorithm) using various filters. Basic statistic evaluation is used to determine which texture is examined. Texture should be identified regardless it's rotation and illumination.
novako20's
LBP-HF
SRC
DOC
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f4: 0
Ondrej Novak:   Local Binary Pattern Histogram Fourier Features

Paper: Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features
Authors: Timo Ahonen, Jiri Matas, Chu He, Matti Pietikainen
Published: 2009
haurvojt's
WLD
version 1.10
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haurvojt's   Weber Local Descriptor with UHoG

The algorithm combines Weber excitation with unsigned Histogram of Gradients.
1.0:plain with detection window 9x9
1.1:plain with detection window 7x7
1.2:plain with detection window 5x5
1.3:histograms of WLD collected on every color plane instead of only the one with biggest histogram
1.4: added normalized histogram of color in the detection window
1.6: changed color to HSV space
1.7: changed back to RGB, added normalized location of pixel to feature vector
1.8: removed location, added pixel color value (with normalization)
1.10: smaller color detection window
magdafried's
CBP
version 0.0.1
WEB
SRC
DOC
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f4: 0
friedmag:   Centralized Binary Patterns

Centralized Binary Patterns Embedded with Image Euclidean Distance for Facial Expression Recognition
Published in: Natural Computation, 2008. ICNC '08, page(s): 115 - 119
ebasaeed's
learnFeaturesRS
version 1
WEB
DOC
f1: 1
f2: 0
f3: 1
f4: 0
Tuia, D.; Volpi, M. ; Dalla Mura, M. ; Rakotomamonjy, A. ; Flamary, R.:   Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM
mevenkamp's
PCA-MS
BIB
WEB
SRC
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Niklas Mevenkamp, Benjamin Berkels:   Variational Multi-Phase Segmentation using High-Dimensional Local Features

A variational multi-phase segmentation framework based on the Mumford-Shah energy, combined with PCA-based dimension reduction is used to segment color or gray-value images into regions of different structure identified by high-dimensional features, such as local spectral histograms (for Texture) and localized Fourier transforms (for Crystals).
pavelm10's
base
f1: 0
f2: 0
f3: 0
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pavelm10's   base
stanej14's
Textons
f1: 1
f2: 0
f3: 0
f4: 0
Manik Varma and Andrew Zisserman:   A Statistical Approach to Texture Classification from Single Images
hejnapet's
Colors
f1: 0
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f3: 0
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hejnapet's   Colors
hejnapet's
Grayscale
f1: 0
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hejnapet's   Grayscale
juzlomar's
Test
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juzlomar's   Test
kratolu3's
Dominant Local Binary Patterns
f1: 0
f2: 0
f3: 0
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kratolu3's   Dominant Local Binary Patterns
wallejak's
Gravitational model
version 1.3
f1: 0
f2: 0
f3: 0
f4: 0
wallejak's   A simplified gravitational model to analyze texture roughness
lorenpe2's
Three-Patch Code
version 1
f1: 0
f2: 0
f3: 0
f4: 0
Wolf, L., Hassner, T., Taigman, Y.:   Wolf, L., Hassner, T., Taigman, Y. (2008) Descriptor based methods in the wild. In ECCV workshop on faces in real-life images: Detection, alignment, and recognition.

Wolf, L., Hassner, T., Taigman, Y. (2008) Descriptor based methods in the wild. In ECCV workshop on faces in real-life images: Detection, alignment, and recognition.
liutoole's
Laws Filter Masks
version 1.1
SRC
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liutoole's   Laws Filter Masks
malicto1's
Test
f1: 0
f2: 0
f3: 0
f4: 0
malicto1's   Vyzkouseni nahrani vysledku s puvodni metodou gaussian blur bez jakekoliv upravy
lorenpe's
TPC
version 1
f1: 0
f2: 0
f3: 0
f4: 0
Wolf, L., Hassner, T., Taigman, Y.:   Three-Patch Code

Wolf, L., Hassner, T., Taigman, Y. (2008) Descriptor based methods in the wild. In ECCV workshop on faces in real-life images: Detection, alignment, and recognition.
lorenpe2@fit.cvut.cz's
Three-Patch Code
version 1
f1: 0
f2: 0
f3: 0
f4: 0
Wolf, L., Hassner, T., Taigman, Y.:   Wolf, L., Hassner, T., Taigman, Y. (2008) Descriptor based methods in the wild. In ECCV workshop on faces in real-life images: Detection, alignment, and recognition.
hajkokla's
monospectral pixelwise
f1: 0
f2: 0
f3: 0
f4: 0
hajkokla's   monospectral pixelwise

[FIT2016] Monospectral pixelwise features.
hajkokla's
Haralick
f1: 0
f2: 0
f3: 0
f4: 0
hajkokla's   Haralick textural features
babicpe1's
Surf
f1: 0
f2: 0
f3: 0
f4: 0
babicpe1's   Surf

SURF alg based on http://www.vision.ee.ethz.ch/~surf/eccv06.pdf
tothmatu's
Test_Farebne
version 1.0
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f2: 0
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tothmatu's   Test_Farebne

Farebne
tothmatu's
Test_Greyscale
version 1.0
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tothmatu's   Test_Greyscale

Grayscale
rumanjak's
CBP
f1: 0
f2: 0
f3: 0
f4: 0
rumanjak's   Centralised Binary Pattern
slavojir's
Wold
f1: 0
f2: 0
f3: 0
f4: 0
slavojir's   Wold
liutoole_roz's
Grey spectrum
version 1.0
f1: 0
f2: 0
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liutoole_roz's   Grey spectrum
juzlomar's
ELTFS
f1: 0
f2: 0
f3: 0
f4: 0
juzlomar's   Enhanced Local Texture Feature Sets
tauchkri's
grayscale
version 0.1
f1: 0
f2: 0
f3: 0
f4: 0
tauchkri:   grayscale

test
tauchkri's
LDP
version 1.0
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f2: 0
f3: 0
f4: 0
tauchkri:   Local Derivative Pattern
conanbest1's
f1: 0
f2: 0
f3: 0
f4: 0
conanbest1's   
malicto1's
Gabor features
f1: 0
f2: 0
f3: 0
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malicto1's   Gabor features
hejnapet's
SIFT
version 14
f1: 0
f2: 0
f3: 0
f4: 0
hejnapet's   SIFT
tothmatu's
HASC
version WANNABE
SRC
f1: 0
f2: 0
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tothmatu's   HASC

HASC
tothmatu's
HASC
version WANNABE_BLUR
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f2: 0
f3: 0
f4: 0
tothmatu's   HASC
tothmatu's
HASC
version COVARIANCE_TRYOUT
f1: 0
f2: 0
f3: 0
f4: 0
tothmatu's   HASC
tothmatu's
HASC
version NORMALIZATION_COVARIANCE
f1: 0
f2: 0
f3: 0
f4: 0
tothmatu's   HASC
tothmatu's
HASC
version COVARIANCE_MUTUAL
f1: 0
f2: 0
f3: 0
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tothmatu's   HASC
siddharth450's
test_algo
version 0.01
SRC
f1: 1
f2: 0
f3: 0
f4: 0
Siddharth Kumar:   test_algo
siddharth's
My algo1
version 0.1
SRC
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f2: 0
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f4: 0
Siddharth Kumar:   My algo1
v-andrearczyk's
FCNT
WEB
DOC
f1: 1
f2: 0
f3: 1
f4: 0
Vincent Andrearczyk, Paul F. Whelan:   Fully Convolutional Network for Texture
v-andrearczyk's
FCNT-unsup
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Vincent Andrearczyk, Paul F. Whelan:   Fully Convolutional Network for Texture unsupervised
emkay's
A3M
version 1.0
BIB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
Martin Kiechle, Martin Storath, Andreas Weinmann, Martin Kleinsteuber:   Model-based learning of local image features for unsupervised texture segmentation

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this paper, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.
huangyuan's
Yuan
version 3
f1: 1
f2: 0
f3: 0
f4: 0
Yuan:   Yuan
alexandliutao@gmail.com's
FSEG_nocolor
f1: 0
f2: 0
f3: 0
f4: 0
alexandliutao@gmail.com's   FSEG_nocolor
alexandliutao@gmail.com's
FSEG_onlycolor
f1: 0
f2: 0
f3: 0
f4: 0
alexandliutao@gmail.com's   FSEG_onlycolor
alexandliutao@gmail.com's
sparse+unsupervised
f1: 0
f2: 0
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alexandliutao@gmail.com's   sparse+unsupervised
alexandliutao@gmail.com's
no_haar
f1: 0
f2: 0
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alexandliutao@gmail.com's   no_haar
alexandliutao@gmail.com's
haar
f1: 0
f2: 0
f3: 0
f4: 0
alexandliutao@gmail.com's   haar
alexandliutao@gmail.com's
filter+sparse
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alexandliutao@gmail.com's   filter+sparse
alexandliutao@gmail.com's
filter+sparse+process
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alexandliutao@gmail.com's   filter+sparse+process
alexandliutao@gmail.com's
atom30t23
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alexandliutao@gmail.com's   atom30t23
alexandliutao@gmail.com's
atom20t14
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f2: 0
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f4: 0
alexandliutao@gmail.com's   atom20t14
alexandliutao@gmail.com's
atom20t6
f1: 0
f2: 0
f3: 0
f4: 0
alexandliutao@gmail.com's   atom20t6
alexandliutao@gmail.com's
atom9t6
f1: 0
f2: 0
f3: 0
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alexandliutao@gmail.com's   atom9t6
alexandliutao@gmail.com's
my_unsupervised
f1: 0
f2: 0
f3: 0
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alexandliutao@gmail.com's   my_unsupervised
alexandliutao@gmail.com's
multiscale
f1: 0
f2: 0
f3: 0
f4: 0
alexandliutao@gmail.com's   multiscale
Ondrej's
f1: 0
f2: 0
f3: 0
f4: 0
Ondrej's   
richtrad's
3DFT
version 0.1
f1: 0
f2: 0
f3: 0
f4: 0
richtrad's   3DFT
th's
texture2015
f1: 0
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th's   texture2015
th's
multi2015
f1: 0
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th's   multi2015
th's
Histology
f1: 0
f2: 0
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th's   Histology
huangyuan's
unet
version psp21ewt
f1: 1
f2: 0
f3: 0
f4: 0
huangyuan's   unet
1354884112@qq.com's
ltsparse
f1: 0
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1354884112@qq.com's   ltsparse
th's
semi
f1: 0
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th's   semi
nemecst4's
segm_tut_101019
version 1
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nemecst4's   segm_tut_101019
pirojan's
MI-ROZ
f1: 0
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pirojan's   MI-ROZ
macaond3's
Roz
f1: 0
f2: 0
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f4: 0
macaond3's   Roz
vanclmil's
helloworld
f1: 0
f2: 0
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vanclmil's   helloworld
strejivo's
segmenter1
version 1.0
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strejivo's   segmenter1
malekva1's
alg1
f1: 0
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malekva1's   alg1
cernyon3's
ALGAC SUPER MEGA
version YES
f1: 0
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cernyon3's   NO
brokejan's
Colors
version 1
f1: 1
f2: 0
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f4: 0
brokejan's   Colors
dressmar's
tmp
version 1
f1: 0
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dressmar's   tmp
lana's
tut2
version 5
SRC
f1: 0
f2: 0
f3: 1
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lana's   tut2
alexama1's
ROZ test
version 0.0.1
f1: 1
f2: 1
f3: 1
f4: 1
Marek Alexa:   Just a test