The Prague Texture Segmentation Datagenerator and Benchmark - Algorithms
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The algorithm features are: f1 = classification (supervised segmentation), f2 = hiearchy result (manual selection), f3 = known number of regions, f4 = [reserved].
chaththa85's
ImprvGMRF
version 2
BIB
DOC
f1: 0
f2: 0
f3: 1
f4: 0
C. Dharmagunawardhana, S. Mahmoodi, M. Niranjan, M. Bennett:   Gaussian Markov random field based improved texture descriptor for image segmentation

An improved semi parametric method of texture feature formulation using GMRFs.

Texture descriptor based on Gaussian Markov random fields (GMRFs). A spatially localized parameter estimation technique using local linear regression is performed and the distributions of local parameter estimates are constructed to formulate the texture features. The inconsistencies arising in localized parameter estimation are addressed by applying generalized inverse, regularization and an estimation window size selection criterion. The texture descriptors are named as local parameter histograms (LPHs) and are used in texture segmentation with the k-means clustering algorithm.

The segmentation results on general texture datasets demonstrate that LPH descriptors significantly improve the performance of classical GMRF features and achieve better results compared to the state-of-the-art texture descriptors based on local feature distributions.


List of uploaded results for 'ImprvGMRF' algorithm
   benchmark label version CS OS US ME NE O C CA CO CC I II EA MS RM CI GCE LCE BCE GBCE BGM SC SSC VD L AVI NVI NMI M ARI JC DC FMI WI WII NBDE
Grayscale [normal] ImprvGMRF 1 54.02 16.37 6.27 17.66 18.37 17.00 21.28 68.30 76.29 82.87 23.71 2.87 77.21 64.44 5.49 78.35 19.99 13.91 32.91 26.82 76.29 70.11 70.17 16.76 74.76 7.29 24.29 73.76 9.77 69.43 62.07 75.43 75.91 80.56 72.26 5.45
Colour [normal] ImprvGMRF 1 49.02 15.06 10.82 21.44 22.76 22.06 26.87 64.74 73.95 79.03 26.05 3.99 73.98 60.92 6.01 75.18 23.07 15.90 36.10 28.92 73.95 67.21 67.44 18.67 72.20 8.14 27.33 70.26 10.99 66.08 59.42 73.13 73.46 76.08 71.54 6.76