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].
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

List of uploaded results for 'GSRM MARKOV sup.' 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 [normal] GSRM MARKOV sup. BHAT area-weighted 64.07 8.59 6.49 18.58 17.64 8.75 9.29 77.86 84.44 87.76 15.56 2.92 84.44 76.66 4.61 85.25 14.13 7.37 24.16 17.40 84.44 79.29 79.28 11.18 84.37 4.61 15.78 82.78 8.07 76.36 70.63 81.52 81.77 81.91 82.14 5.32
Colour [normal] GSRM MARKOV sup. KL area-weighted 67.55 11.36 9.11 12.58 13.54 7.81 8.42 78.07 84.30 87.19 15.70 2.91 84.32 76.44 4.26 85.01 13.33 7.21 23.13 17.01 84.30 79.53 79.83 10.85 84.77 4.41 14.60 83.91 7.61 77.27 71.45 82.06 82.33 82.59 82.65 5.06
#Colour [normal] GSRM MARKOV sup. BHAT area-unweighted 28.66 5.38 56.22 9.41 7.90 48.30 30.54 46.74 61.85 66.02 38.15 15.82 53.90 42.78 15.39 57.32 10.00 5.31 48.97 44.27 61.85 58.60 52.64 21.45 59.55 8.51 28.23 58.26 32.80 39.80 44.33 58.53 62.95 46.63 89.92 10.85
#Colour [normal] GSRM MARKOV sup. KL area-unweighted 21.25 6.13 49.95 24.57 23.37 60.05 40.84 37.52 53.27 61.10 46.73 18.42 45.16 29.90 19.35 49.58 12.47 6.54 57.52 51.59 53.27 49.28 44.10 26.02 53.18 10.28 34.43 49.28 41.93 27.97 37.24 50.96 56.40 38.90 87.22 13.06