The Prague Texture Segmentation Datagenerator and Benchmark - Results
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Results are sorted by criterion 'CA' (class accuracy) in descending order. The sorting criterium or the order can be changed by clicking on the appropriate criterium label. Subset of compared results can be filtered by several filters below. The segmentation details (single mosaics and their corresponding criteria values) are visible by clicking on the order number. Below the criterium labels are mean and standard deviation of the values of displayed results. They are used to compute z-scores which are displayed in brackets. The ranks are displayed in parentheses.

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? 0 1   f1 = classification (supervised segmentation)
? 0 1   f2 = hiearchy result (manual selection)
? 0 1   f3 = known number of regions
? 0 1   f4 = [reserved]
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Algorithm: Version:

   
Benchmark - Colour f1 f2 f3 f4 AVG
RANK
NORM		 CS
54.64
±18.28		 OS
12.85
±2.29		 US
11.99
±6.23		 ME
22.64
±20.84		 NE
22.91
±21.01		 O
15.20
±12.17		 C
17.68
±12.98		 criterion 'CA' (class accuracy) in descending orderCA
69.94
±12.66		 CO
77.90
±10.60		 CC
82.13
±8.31		 I
22.10
±10.60		 II
4.31
±3.23		 EA
77.60
±10.56		 MS
67.44
±15.22		 RM
6.37
±3.16		 CI
78.68
±10.09		 GCE
18.58
±10.60		 LCE
12.35
±10.03		 BCE
30.99
±12.71		 GBCE
24.76
±12.14		 BGM
77.90
±10.60		 SC
72.12
±12.49		 SSC
72.02
±12.52		 VD
16.07
±9.41		 L
77.42
±10.62		 AVI
6.96
±4.66		 NVI
21.20
±10.97		 NMI
76.11
±12.84		 M
10.65
±6.08		 ARI
69.59
±14.87		 JC
64.20
±13.65		 DC
75.93
±12.08		 FMI
76.31
±11.95		 WI
76.28
±12.02		 WII
77.13
±11.95		 NBDE
7.02
±3.35
 1.  frzn's texNCUT   [large] 0 0 1 0  87.01 
 (1.64) 
 [0.740] 		 72.54
(1)
[0.979]		 10.92
(2)
[-0.840]		 9.61
(4)
[-0.382]		 10.25
(2)
[-0.594]		 9.83
(2)
[-0.622]		 7.33
(2)
[-0.646]		 8.17
(3)
[-0.733]		 80.58
(1)
[0.841]		 86.89
(1)
[0.848]		 88.28
(2)
[0.740]		 13.11
(1)
[-0.848]		 2.36
(2)
[-0.604]		 86.39
(1)
[0.832]		 80.33
(1)
[0.847]		 3.69
(1)
[-0.849]		 86.97
(1)
[0.821]		 11.92
(2)
[-0.628]		 6.85
(2)
[-0.548]		 20.64
(1)
[-0.815]		 15.56
(1)
[-0.757]		 86.89
(1)
[0.848]		 82.46
(1)
[0.828]		 81.90
(1)
[0.789]		 9.18
(1)
[-0.732]		 84.35
(4)
[0.652]		 4.30
(3)
[-0.570]		 14.49
(3)
[-0.611]		 84.14
(3)
[0.626]		 6.03
(1)
[-0.758]		 81.48
(1)
[0.799]		 75.77
(1)
[0.848]		 85.35
(1)
[0.779]		 85.50
(1)
[0.769]		 84.76
(2)
[0.705]		 86.57
(1)
[0.790]		 4.49
(1)
[-0.754]
 2.  xiaofang's sas_gmm(color) c=7  [normal] 0 0 1 0  86.71 
 (2.14) 
 [0.674] 		 66.88
(4)
[0.670]		 13.63
(5)
[0.342]		 11.93
(7)
[-0.010]		 8.46
(1)
[-0.680]		 8.44
(1)
[-0.688]		 4.92
(1)
[-0.844]		 6.97
(2)
[-0.825]		 79.87
(2)
[0.785]		 85.76
(2)
[0.742]		 88.16
(3)
[0.725]		 14.24
(2)
[-0.742]		 2.36
(3)
[-0.603]		 85.72
(2)
[0.768]		 78.65
(2)
[0.736]		 4.45
(3)
[-0.610]		 86.33
(2)
[0.757]		 11.00
(1)
[-0.715]		 5.90
(1)
[-0.643]		 21.14
(2)
[-0.775]		 16.05
(2)
[-0.717]		 85.76
(2)
[0.742]		 81.49
(2)
[0.750]		 81.61
(2)
[0.766]		 9.50
(2)
[-0.698]		 85.88
(1)
[0.796]		 3.88
(1)
[-0.660]		 13.10
(1)
[-0.738]		 85.65
(1)
[0.743]		 6.52
(2)
[-0.679]		 80.86
(2)
[0.758]		 75.31
(2)
[0.814]		 85.05
(2)
[0.755]		 85.32
(2)
[0.753]		 85.89
(1)
[0.799]		 85.29
(2)
[0.682]		 5.58
(4)
[-0.430]
 3.  felipecalderero's GSRM sup. KL area-weighted  [normal] 0 0 1 0  85.76 
 (2.69) 
 [0.666] 		 68.72
(2)
[0.770]		 9.00
(1)
[-1.681]		 6.67
(1)
[-0.855]		 15.09
(4)
[-0.362]		 15.16
(4)
[-0.369]		 7.74
(3)
[-0.612]		 6.79
(1)
[-0.839]		 78.90
(3)
[0.708]		 84.74
(3)
[0.646]		 89.30
(1)
[0.863]		 15.26
(3)
[-0.646]		 2.10
(1)
[-0.686]		 85.01
(3)
[0.702]		 77.12
(3)
[0.635]		 4.54
(4)
[-0.580]		 85.98
(3)
[0.723]		 13.29
(3)
[-0.499]		 6.93
(3)
[-0.540]		 22.51
(3)
[-0.667]		 16.16
(3)
[-0.709]		 84.74
(3)
[0.646]		 80.09
(3)
[0.638]		 80.13
(3)
[0.648]		 10.88
(4)
[-0.552]		 84.38
(3)
[0.654]		 4.29
(2)
[-0.571]		 14.22
(2)
[-0.636]		 84.50
(2)
[0.654]		 6.84
(3)
[-0.625]		 78.95
(3)
[0.629]		 73.34
(3)
[0.669]		 83.20
(3)
[0.601]		 83.40
(3)
[0.593]		 84.46
(3)
[0.680]		 82.76
(3)
[0.471]		 4.96
(2)
[-0.615]
 4.  felipecalderero's GSRM MARKOV sup. KL area-weighted  [normal] 0 0 1 0  85.13 
 (3.69) 
 [0.575] 		 67.55
(3)
[0.706]		 11.36
(3)
[-0.649]		 9.11
(3)
[-0.464]		 12.58
(3)
[-0.482]		 13.54
(3)
[-0.446]		 7.81
(4)
[-0.607]		 8.42
(4)
[-0.713]		 78.07
(4)
[0.643]		 84.30
(4)
[0.604]		 87.19
(4)
[0.608]		 15.70
(4)
[-0.604]		 2.91
(4)
[-0.433]		 84.32
(4)
[0.636]		 76.44
(4)
[0.591]		 4.26
(2)
[-0.668]		 85.01
(4)
[0.627]		 13.33
(4)
[-0.495]		 7.21
(4)
[-0.513]		 23.13
(4)
[-0.618]		 17.01
(4)
[-0.638]		 84.30
(4)
[0.604]		 79.53
(4)
[0.593]		 79.83
(4)
[0.624]		 10.85
(3)
[-0.555]		 84.77
(2)
[0.691]		 4.41
(4)
[-0.547]		 14.60
(4)
[-0.602]		 83.91
(4)
[0.608]		 7.61
(4)
[-0.499]		 77.27
(4)
[0.517]		 71.45
(4)
[0.531]		 82.06
(4)
[0.507]		 82.33
(4)
[0.504]		 82.59
(4)
[0.524]		 82.65
(4)
[0.461]		 5.06
(3)
[-0.584]
 5.  david0432's Cooperative Mum-Shah   [normal] 0 0 1 0  80.21 
 (5.28) 
 [0.096] 		 53.73
(5)
[-0.050]		 16.29
(8)
[1.507]		 11.76
(6)
[-0.037]		 18.57
(6)
[-0.195]		 18.66
(6)
[-0.202]		 12.55
(5)
[-0.217]		 16.85
(5)
[-0.064]		 71.58
(5)
[0.130]		 78.99
(5)
[0.103]		 84.91
(5)
[0.335]		 21.01
(5)
[-0.103]		 3.11
(5)
[-0.373]		 79.39
(5)
[0.170]		 70.84
(5)
[0.223]		 6.45
(6)
[0.024]		 80.61
(5)
[0.191]		 16.29
(6)
[-0.216]		 8.88
(5)
[-0.346]		 30.80
(5)
[-0.015]		 23.38
(5)
[-0.113]		 78.99
(5)
[0.103]		 72.83
(5)
[0.057]		 73.35
(5)
[0.106]		 14.43
(5)
[-0.174]		 79.26
(5)
[0.173]		 5.92
(6)
[-0.222]		 18.40
(5)
[-0.255]		 78.23
(5)
[0.166]		 10.09
(5)
[-0.092]		 70.52
(5)
[0.063]		 64.26
(5)
[0.004]		 76.88
(5)
[0.079]		 77.31
(5)
[0.084]		 77.60
(5)
[0.109]		 77.93
(6)
[0.066]		 6.15
(5)
[-0.259]
 6.  xiaofang's SR_multifeat all  [normal] 0 0 1 0  77.58 
 (6.17) 
 [-0.177] 		 45.57
(7)
[-0.496]		 14.56
(6)
[0.749]		 27.86
(8)
[2.549]		 17.99
(5)
[-0.223]		 17.58
(5)
[-0.253]		 14.76
(6)
[-0.036]		 20.63
(6)
[0.227]		 65.36
(6)
[-0.361]		 76.12
(6)
[-0.167]		 76.95
(7)
[-0.623]		 23.88
(6)
[0.167]		 5.18
(7)
[0.270]		 73.17
(7)
[-0.419]		 64.18
(6)
[-0.214]		 7.49
(7)
[0.353]		 74.36
(7)
[-0.428]		 14.68
(5)
[-0.368]		 9.40
(6)
[-0.294]		 32.16
(6)
[0.092]		 26.88
(6)
[0.175]		 76.12
(6)
[-0.167]		 71.39
(6)
[-0.058]		 70.07
(6)
[-0.156]		 15.46
(6)
[-0.065]		 76.62
(6)
[-0.076]		 5.91
(5)
[-0.224]		 19.57
(6)
[-0.149]		 77.52
(6)
[0.111]		 11.17
(7)
[0.085]		 68.58
(6)
[-0.068]		 62.59
(6)
[-0.118]		 75.81
(6)
[-0.010]		 76.54
(6)
[0.019]		 72.03
(7)
[-0.354]		 82.58
(5)
[0.455]		 7.63
(7)
[0.183]
 7.  chaththa85's ImprvGMRF 1  [normal] 0 0 1 0  75.46 
 (6.67) 
 [-0.304] 		 49.02
(6)
[-0.308]		 15.06
(7)
[0.969]		 10.82
(5)
[-0.189]		 21.44
(7)
[-0.058]		 22.76
(7)
[-0.007]		 22.06
(7)
[0.564]		 26.87
(7)
[0.707]		 64.74
(7)
[-0.411]		 73.95
(7)
[-0.372]		 79.03
(6)
[-0.373]		 26.05
(7)
[0.372]		 3.99
(6)
[-0.099]		 73.98
(6)
[-0.343]		 60.92
(7)
[-0.428]		 6.01
(5)
[-0.116]		 75.18
(6)
[-0.347]		 23.07
(7)
[0.424]		 15.90
(7)
[0.354]		 36.10
(7)
[0.402]		 28.92
(7)
[0.343]		 73.95
(7)
[-0.372]		 67.21
(7)
[-0.393]		 67.44
(7)
[-0.366]		 18.67
(7)
[0.276]		 72.20
(7)
[-0.492]		 8.14
(7)
[0.255]		 27.33
(7)
[0.559]		 70.26
(7)
[-0.456]		 10.99
(6)
[0.056]		 66.08
(7)
[-0.236]		 59.42
(7)
[-0.351]		 73.13
(7)
[-0.232]		 73.46
(7)
[-0.238]		 76.08
(6)
[-0.017]		 71.54
(7)
[-0.468]		 6.76
(6)
[-0.075]
 8.  Alojz's TAlgorithm 0.1  [normal] 0 0 1 0  52.97 
 (7.72) 
 [-2.270] 		 13.11
(8)
[-2.271]		 11.94
(4)
[-0.398]		 8.18
(2)
[-0.612]		 76.71
(8)
[2.594]		 77.30
(8)
[2.588]		 44.41
(8)
[2.400]		 46.76
(8)
[2.239]		 40.40
(8)
[-2.334]		 52.42
(8)
[-2.402]		 63.23
(8)
[-2.274]		 47.58
(8)
[2.402]		 12.47
(8)
[2.528]		 52.83
(8)
[-2.345]		 31.07
(8)
[-2.389]		 14.09
(8)
[2.446]		 55.02
(8)
[-2.344]		 45.06
(8)
[2.498]		 37.72
(8)
[2.530]		 61.44
(8)
[2.396]		 54.10
(8)
[2.417]		 52.42
(8)
[-2.402]		 41.97
(8)
[-2.415]		 41.82
(8)
[-2.411]		 39.59
(8)
[2.501]		 51.93
(8)
[-2.399]		 18.79
(8)
[2.540]		 47.89
(8)
[2.432]		 44.63
(8)
[-2.452]		 25.93
(8)
[2.511]		 32.97
(8)
[-2.462]		 31.47
(8)
[-2.398]		 45.98
(8)
[-2.479]		 46.62
(8)
[-2.484]		 46.86
(8)
[-2.447]		 47.76
(8)
[-2.459]		 15.50
(8)
[2.534]