The Prague Texture Segmentation Datagenerator and Benchmark - Results
optimized
for

Results are sorted by criterion 'VD' (Van Dongen metric) in ascending 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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Filter by algorithm features
? 0 1   f1 = classification (supervised segmentation)
? 0 1   f2 = hiearchy result (manual selection)
? 0 1   f3 = known number of regions
? 0 1   f4 = [reserved]
Filter by degradationtype: ? no Gaussian Poisson Salt&pepper Blur (Gaussian) Median
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Algorithm: Version:

   
Benchmark - Colour f1 f2 f3 f4 AVG
RANK
NORM
CS
29.95
±17.91
OS
28.92
±33.71
US
24.67
±32.15
ME
47.81
±28.49
NE
47.71
±28.60
O
41.64
±26.89
C
46.84
±35.67
CA
49.01
±21.91
CO
57.02
±24.97
CC
64.63
±28.05
I
42.98
±24.97
II
20.20
±33.53
EA
57.41
±24.79
MS
38.57
±37.97
RM
22.08
±32.65
CI
58.97
±25.37
GCE
34.69
±28.14
LCE
29.64
±30.29
BCE
52.47
±21.48
GBCE
47.42
±23.67
BGM
57.02
±24.97
SC
50.63
±23.04
SSC
50.83
±22.59
criterion 'VD' (Van Dongen metric) in ascending orderVD
34.99
±27.75
L
57.33
±24.68
AVI
23.49
±32.10
NVI
38.26
±26.34
NMI
56.66
±24.78
M
28.70
±29.90
ARI
46.39
±21.36
JC
42.35
±19.42
DC
54.09
±23.79
FMI
55.27
±24.15
WI
56.48
±24.87
WII
56.67
±26.59
NBDE
24.28
±31.77
 1.  xiaofang's LocalGlobalGraph color  [normal] 0 1 0 0  76.67 
 (1.92) 
 [0.693] 
41.42
(3)
[0.641]
15.04
(4)
[-0.412]
12.48
(4)
[-0.379]
27.64
(3)
[-0.708]
26.92
(2)
[-0.727]
17.80
(1)
[-0.887]
15.13
(3)
[-0.889]
66.53
(2)
[0.800]
75.75
(2)
[0.750]
82.19
(2)
[0.626]
24.25
(2)
[-0.750]
4.17
(3)
[-0.478]
76.10
(1)
[0.754]
63.63
(2)
[0.660]
6.72
(2)
[-0.470]
77.48
(1)
[0.730]
20.47
(4)
[-0.505]
11.25
(1)
[-0.607]
35.25
(2)
[-0.801]
26.03
(1)
[-0.904]
75.75
(2)
[0.750]
68.47
(3)
[0.774]
68.43
(2)
[0.779]
17.13
(1)
[-0.644]
75.42
(1)
[0.733]
6.47
(1)
[-0.530]
21.84
(2)
[-0.623]
76.07
(1)
[0.783]
11.22
(1)
[-0.585]
66.22
(1)
[0.928]
59.42
(1)
[0.879]
73.37
(1)
[0.810]
73.72
(1)
[0.764]
74.33
(2)
[0.718]
73.83
(3)
[0.646]
8.02
(1)
[-0.512]
 2.  scarpa's TFR/KLD   [normal] 0 1 0 0  76.65 
 (2.11) 
 [0.692] 
51.25
(1)
[1.189]
5.84
(3)
[-0.685]
7.16
(3)
[-0.545]
31.64
(4)
[-0.567]
31.38
(4)
[-0.571]
19.65
(2)
[-0.818]
9.67
(1)
[-1.042]
67.45
(1)
[0.842]
76.40
(1)
[0.776]
81.12
(3)
[0.588]
23.60
(1)
[-0.776]
4.09
(2)
[-0.481]
75.80
(2)
[0.742]
65.19
(1)
[0.701]
7.21
(3)
[-0.455]
77.21
(2)
[0.719]
20.36
(3)
[-0.509]
14.36
(4)
[-0.504]
34.33
(1)
[-0.844]
28.34
(2)
[-0.806]
76.40
(1)
[0.776]
69.58
(1)
[0.822]
69.20
(1)
[0.813]
18.01
(2)
[-0.612]
74.35
(2)
[0.689]
7.63
(2)
[-0.494]
24.48
(3)
[-0.523]
71.43
(2)
[0.596]
12.64
(2)
[-0.537]
63.75
(2)
[0.813]
58.96
(2)
[0.855]
71.77
(2)
[0.743]
72.35
(2)
[0.707]
69.11
(3)
[0.508]
76.80
(2)
[0.757]
8.38
(3)
[-0.500]
 3.  scarpa's TFR  [normal] 0 1 0 0  73.61 
 (2.97) 
 [0.571] 
46.13
(2)
[0.903]
2.37
(1)
[-0.788]
23.99
(5)
[-0.021]
26.70
(2)
[-0.741]
25.23
(1)
[-0.786]
28.73
(3)
[-0.480]
12.50
(2)
[-0.963]
61.32
(3)
[0.562]
73.00
(3)
[0.640]
68.91
(4)
[0.152]
27.00
(3)
[-0.640]
8.56
(5)
[-0.347]
68.62
(3)
[0.453]
59.76
(3)
[0.558]
8.61
(4)
[-0.412]
69.73
(4)
[0.424]
15.51
(1)
[-0.681]
12.03
(3)
[-0.581]
37.29
(3)
[-0.707]
33.80
(3)
[-0.575]
73.00
(3)
[0.640]
68.48
(2)
[0.775]
64.83
(3)
[0.620]
18.21
(3)
[-0.605]
68.54
(4)
[0.454]
7.73
(3)
[-0.491]
25.28
(4)
[-0.493]
68.13
(4)
[0.462]
17.47
(4)
[-0.375]
57.90
(3)
[0.539]
55.57
(3)
[0.680]
68.82
(3)
[0.619]
70.72
(3)
[0.640]
61.21
(4)
[0.190]
84.40
(1)
[1.043]
8.05
(2)
[-0.511]
 4.  test's SWA def_par  [normal] 0 1 0 0  66.59 
 (3.22) 
 [0.303] 
27.06
(4)
[-0.161]
50.21
(5)
[0.631]
4.53
(1)
[-0.626]
25.76
(1)
[-0.774]
27.50
(3)
[-0.707]
33.01
(4)
[-0.321]
85.19
(5)
[1.075]
54.84
(4)
[0.266]
60.67
(4)
[0.146]
88.17
(1)
[0.839]
39.33
(4)
[-0.146]
2.11
(1)
[-0.539]
66.94
(4)
[0.385]
53.71
(4)
[0.399]
6.11
(1)
[-0.489]
70.32
(3)
[0.447]
17.27
(2)
[-0.619]
11.50
(2)
[-0.599]
50.96
(4)
[-0.070]
45.18
(4)
[-0.094]
60.67
(4)
[0.146]
51.56
(4)
[0.040]
55.84
(4)
[0.222]
24.20
(4)
[-0.389]
69.05
(3)
[0.475]
9.75
(4)
[-0.428]
21.56
(1)
[-0.634]
70.86
(3)
[0.573]
13.68
(3)
[-0.502]
51.67
(4)
[0.247]
43.43
(4)
[0.055]
58.77
(4)
[0.196]
61.69
(4)
[0.266]
79.03
(1)
[0.907]
51.05
(4)
[-0.211]
10.34
(4)
[-0.439]
 5.  xiaofang's LocalGlobalGraph color  [normal] 0 1 0 0  53.30 
 (4.78) 
 [-0.229] 
8.88
(5)
[-1.176]
5.06
(2)
[-0.708]
4.85
(2)
[-0.616]
80.13
(5)
[1.134]
80.24
(5)
[1.138]
55.66
(5)
[0.521]
63.52
(4)
[0.468]
39.03
(5)
[-0.455]
51.33
(5)
[-0.228]
62.45
(5)
[-0.078]
48.67
(5)
[0.228]
7.25
(4)
[-0.386]
52.03
(5)
[-0.217]
31.73
(5)
[-0.180]
8.75
(5)
[-0.408]
54.15
(5)
[-0.190]
39.38
(5)
[0.167]
33.62
(5)
[0.132]
61.80
(5)
[0.434]
56.04
(5)
[0.364]
51.33
(5)
[-0.228]
40.82
(5)
[-0.426]
41.81
(5)
[-0.399]
37.34
(5)
[0.085]
51.70
(5)
[-0.228]
14.32
(5)
[-0.286]
41.17
(5)
[0.110]
48.75
(5)
[-0.319]
22.10
(5)
[-0.221]
33.98
(5)
[-0.581]
32.00
(5)
[-0.533]
46.97
(5)
[-0.300]
48.28
(5)
[-0.290]
50.31
(5)
[-0.248]
49.07
(5)
[-0.286]
15.82
(5)
[-0.266]
 6.  xiaofang's LocalGlobalGraph color  [normal] 0 1 0 0  3.59 
 (6.00) 
 [-2.029] 
4.94
(6)
[-1.396]
95.00
(6)
[1.960]
95.00
(6)
[2.188]
95.00
(6)
[1.656]
95.00
(6)
[1.653]
95.00
(6)
[1.985]
95.00
(6)
[1.350]
4.88
(6)
[-2.014]
4.94
(6)
[-2.086]
4.94
(6)
[-2.128]
95.06
(6)
[2.086]
95.03
(6)
[2.232]
4.94
(6)
[-2.117]
-42.59
(6)
[-2.138]
95.05
(6)
[2.235]
4.94
(6)
[-2.130]
95.12
(6)
[2.147]
95.06
(6)
[2.160]
95.18
(6)
[1.988]
95.12
(6)
[2.015]
4.94
(6)
[-2.086]
4.88
(6)
[-1.985]
4.88
(6)
[-2.034]
95.06
(6)
[2.165]
4.94
(6)
[-2.122]
95.04
(6)
[2.229]
95.25
(6)
[2.163]
4.75
(6)
[-2.095]
95.09
(6)
[2.220]
4.81
(6)
[-1.946]
4.75
(6)
[-1.936]
4.87
(6)
[-2.069]
4.87
(6)
[-2.086]
4.87
(6)
[-2.075]
4.87
(6)
[-1.948]
95.07
(6)
[2.228]