The Prague Texture Segmentation Datagenerator and Benchmark - Algorithms
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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].
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


List of uploaded results for 'CGCHi' 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
Colour [large] CGCHI 10.94 2.19 3.96 81.91 81.39 59.33 51.77 35.62 50.50 49.27 49.50 10.69 47.04 26.89 10.28 48.39 42.35 38.59 62.15 58.39 50.50 41.93 39.53 40.15 48.15 15.31 50.16 39.47 25.32 30.89 31.50 46.39 47.54 43.30 54.28 10.44
Colour [large] CGCHI 10.94 2.19 3.96 81.91 81.39 59.33 51.77 35.62 50.50 49.27 49.50 10.69 47.04 26.89 10.28 48.39 42.35 38.59 62.15 58.39 50.50 41.93 39.53 40.15 48.15 15.31 50.16 39.47 25.32 30.89 31.50 46.39 47.54 43.30 54.28 10.44