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image
imagewidth (px)
240
1.02k
complexity
float64
0
1
# of SAM segmentations
int64
5
685
# of FC-CLIP classes
int64
1
22
symmetry
float64
0.67
0.94
clutter
float64
1
80
IC9600
float64
0.09
0.87
MSG
float64
0.02
0.82
edge density
float64
0
0.23
MUC1
float64
2
8
MUC2
float64
4
56.3
MUC3
float64
7.4
339
MUC4
float64
14.8
1.87k
MUC5
float64
29.5
9.56k
MUC6
float64
57.2
38.1k
MUC7
float64
108
108k
MUC8
float64
191
159k
gemini surprise score
int64
10
95
AMNet memorability
float64
0.57
0.88
M1
float64
0.01
1.92
M2
float64
0.74
1
M3
float64
0.05
0.98
M4
float64
0.73
1
M5
float64
0.35
0.48
M6
float64
0
0.21
M7
float64
0.08
0.5
M9
float64
0
0.91
M10
float64
16
2.61k
M11
float64
-3.14
1.82
human surprise score
float64
0
1
0.5263
189
12
0.686641
61
0.637635
0.679802
0.152925
8
42.7
194.4
924.5
3,865.1
11,838.8
25,091.9
32,209.6
65
0.775035
1.138421
0.915198
0.087403
0.817538
0.466443
0.125667
0.335422
0.348267
null
null
0.095861
0.322
86
11
0.899839
18
0.414364
0.192873
0.012872
8
38.3
149.3
631.5
2,506.4
8,987.2
30,825.7
59,367
75
0.82675
0.17286
0.964727
0.147582
0.923932
0.438642
0.020406
0.240465
0.272038
866
-0.156047
0.731467
0.8461
190
11
0.827656
35
0.723273
0.381215
0.068172
6.8
30.1
158.5
834.8
4,478.5
20,491.9
67,940.7
103,193.5
75
0.746696
0.459887
0.930214
0.164913
0.871557
0.464602
0.079016
0.305349
0.442032
1,093
-0.882997
0.552563
0.7341
188
13
null
null
0.561386
0.248041
0.035659
8
32.8
135.6
669.8
3,384
14,510.1
47,333.9
79,934.3
75
0.739032
0.104381
0.985676
0.164229
0.957582
0.34638
0.026279
0.170461
0.23603
861
-1.11797
0.667904
0.6534
315
16
0.796552
71
0.805667
0.475411
0.094303
8
50.1
304.5
1,787.4
8,853.3
31,813.1
85,513.2
127,698.4
45
0.689376
0.376399
0.962641
0.08419
0.884033
0.438235
0.084684
0.315793
0.400852
2,555
-1.08552
0.200763
0.5809
213
11
0.831922
47
0.552509
0.374422
0.069925
8
51.6
303.2
1,780.3
9,305.7
38,068.4
107,883.9
158,542.8
75
0.789051
0.363994
0.958326
0.085064
0.878683
0.434726
0.059273
0.32794
0.609208
2,208
-2.26913
0.541222
0.3404
183
12
null
null
0.510377
0.259011
0.036002
8
36.4
154.9
761.7
3,570.9
14,731.7
47,046.2
77,586.9
75
0.793874
0.260148
0.968144
0.109942
0.908592
0.449119
0.032538
0.285404
0.249306
983
-0.670325
0.5389
0.8346
303
19
null
null
0.824843
0.421061
0.092857
8
53.8
299.2
1,562
7,339.2
27,415.8
75,287.3
110,627.3
75
0.660281
0.372894
0.932122
0.107938
0.883426
0.439528
0.088711
0.294521
0.343648
2,070
-1.01147
0.344251
0.6419
180
13
0.863257
40
0.526645
0.333693
0.039291
8
44.6
222
1,001.6
4,383.1
17,084.1
55,245.9
90,736.6
75
0.742237
0.209103
0.950756
0.175954
0.910211
0.420588
0.037618
0.236417
0.309676
1,306
-0.459797
0.809526
0.8667
392
14
0.725017
61
0.867005
0.69346
0.164785
8
49.4
289.1
1,624.9
7,883.5
29,562
78,774.7
118,031.6
65
0.680366
1.123241
0.891409
0.06629
0.804361
0.462236
0.139111
0.32342
0.350831
2,126
-1.09309
0.179311
0.2708
33
4
0.868545
8
0.377537
0.22962
0.046907
7.3
23
74.3
279.7
1,089.1
4,428.8
14,884.7
23,027.3
35
0.567623
0.202128
0.946023
0.238633
0.917474
0.463636
0.080775
0.173003
0.528161
366
-0.292698
0.172014
0.5275
117
12
0.793355
32
0.520846
0.398852
0.15956
5.7
21.7
73.9
261.5
862.5
2,651.2
6,462.2
8,794.2
75
0.752536
0.42199
0.920532
0.310294
0.899734
0.466292
0.07456
0.275556
0.190481
377
-1.61218
0.824417
0.5949
142
10
0.862328
32
0.561661
0.30007
0.125155
8
45.4
237.5
1,309
6,537.9
25,229.4
64,522.1
89,403.9
75
0.819831
0.431127
0.900223
0.237231
0.88937
0.447605
0.053035
0.268718
0.355407
1,744
-1.93176
0.662348
0.6297
224
11
0.833068
54
0.675376
0.372988
0.051318
7.9
38.4
178.8
855.6
3,981.3
16,865.3
56,553.4
88,343.3
65
0.727761
0.347633
0.958553
0.087963
0.890741
0.435737
0.05415
0.194356
0.318287
1,054
-0.635884
0.454737
0.6821
139
15
0.831575
36
0.538471
0.3542
0.075649
8
49
257.2
1,341.4
6,279.4
23,396.3
64,278.2
99,175.8
75
0.775497
0.376662
0.963597
0.082
0.862428
0.449086
0.060247
0.250467
0.21612
1,748
-0.935518
0.704114
0.7063
200
7
0.809688
55
0.648864
0.461434
0.065183
8
33.6
139.4
607.6
2,560.8
9,838.9
32,267.7
52,961.5
85
0.696989
0.370428
0.967983
0.110111
0.887242
0.421405
0.078892
0.193822
0.122714
801
-0.483573
0.747235
0.9649
340
11
0.812412
50
0.716537
0.422747
0.114625
8
48.2
248
1,300.9
6,217.6
23,918.7
66,434.5
97,626.8
85
0.716048
0.417065
0.952638
0.128515
0.886929
0.451697
0.081281
0.29626
0.386355
1,772
-1.28334
0.672211
0.6405
162
5
0.820518
56
0.578857
0.384799
0.09663
7.9
35.7
167.1
765.5
3,348.5
12,620.9
38,019.2
60,659.5
85
0.780165
0.290617
0.977235
0.111746
0.904668
0.429795
0.066792
0.19457
0.213677
1,003
-1.13604
0.672164
0.761
247
12
null
null
0.674347
0.287297
0.086164
7.9
35.3
152.5
687.1
3,032.7
12,041.3
37,125.2
60,848.5
75
0.723774
0.195517
0.976029
0.232432
0.934864
0.394256
0.04193
0.220824
0.18232
899
-0.926971
0.708225
0.4473
88
7
null
null
0.523552
0.471577
0.060664
7.9
35.6
169.8
820.1
3,462
10,062.7
18,286.8
21,491.9
35
0.848247
0.341196
0.957844
0.169952
0.901333
0.428571
0.073906
0.213121
0.71862
1,053
0.12086
0.42706
0.5257
211
13
0.849332
51
0.622792
0.326884
0.055583
8
38.9
155.6
651.8
2,613.3
9,337.5
26,735
40,328.6
75
0.755169
0.309018
0.940146
0.144508
0.906344
0.437239
0.05404
0.20621
0.357537
885
-1.12298
0.710072
0.545
225
14
null
null
0.629555
0.408834
0.095615
8
29.1
117
492.2
2,098.9
8,071.9
23,109.7
34,274.5
75
0.680632
0.666622
0.887026
0.113322
0.815303
0.475806
0.070727
0.389635
0.151725
720
-0.557451
0.653665
0.1493
92
8
0.835449
33
0.37162
0.315657
0.054553
8
29.9
124.6
533.8
2,201.2
7,380.1
18,324.1
27,004.1
75
0.751131
0.34626
0.879592
0.233903
0.883439
0.469697
0.039508
0.341187
0.458422
752
-0.143373
0.254901
0.2165
57
4
0.883842
18
0.354814
0.219448
0.027696
7.7
27.7
107.5
448.5
1,804.6
5,959
15,988.9
25,587.8
10
0.797028
0.137715
0.908286
0.579228
0.949456
0.456989
0.02624
0.280587
0.34559
665
-0.082724
0.244385
0.202
88
2
0.859283
21
0.363371
0.224812
0.044192
7.2
22.4
79.6
293.4
1,127.7
4,027.8
11,958.7
18,952.8
35
0.849312
0.140852
0.986705
0.205003
0.955729
0.430303
0.012547
0.194767
0.165608
398
-0.486956
0.428569
0.3023
43
6
0.793175
48
0.360934
0.416992
0.093349
8
34.4
152.8
729.5
3,444.1
13,223.9
34,024.7
44,853.8
20
0.786179
0.445552
0.9599
0.114449
0.871683
0.454301
0.064965
0.284901
0.393253
927
-0.820431
0.045587
0.7995
273
4
null
null
0.730222
0.563615
0.135746
8
40.8
206.8
1,004.4
4,427.7
16,127.3
39,669.6
51,452.8
85
0.768481
1.405391
0.870294
0.112851
0.821138
0.473094
0.110292
0.355549
0.397689
1,376
-1.21222
0.776968
0.5539
204
18
0.891917
11
0.620115
0.174024
0.021797
6
19.2
64.2
248.9
1,031.3
4,080.9
13,600.9
22,113.9
85
0.822963
0.082581
0.980335
0.170812
0.961303
0.403226
0.033621
0.168532
0.410628
325
0.824402
0.978813
0.2721
64
4
0.822692
28
0.436809
0.354801
0.086283
6.9
22.6
85.7
372.9
1,811.8
7,836.7
23,359.5
33,058.8
65
0.810931
0.297342
0.966364
0.100507
0.9036
0.442408
0.068803
0.235874
0.321706
498
-1.22166
0.656609
0.0516
18
2
0.923099
5
0.197402
0.065223
0.006523
6.9
23.5
71.8
238.1
665.5
1,623.4
4,319.8
7,487
15
0.734423
0.062834
0.919436
0.601439
0.980985
0.460606
0.004024
0.149147
0.424003
389
1.15886
0.044328
0.2082
39
3
0.84459
15
0.361324
0.300373
0.052865
6.5
19.7
54.5
186.6
765.9
3,228.4
12,162.2
21,164.8
10
0.726953
0.548605
0.894466
0.168032
0.86487
0.472727
0.069556
0.28772
0.247858
261
0.07469
0.212088
0.8069
327
13
null
null
0.824153
0.59052
0.139881
8
39.4
176.6
735.3
2,750.8
8,842.8
23,037.8
32,826.3
35
0.742716
1.149637
0.835766
0.061972
0.778649
0.477987
0.156813
0.4006
0.213279
1,133
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0.319028
0.7453
446
10
0.805996
36
0.802321
0.405497
0.113424
8
31.2
117.1
464.3
1,808.6
6,194
18,161.8
28,660.4
75
0.708139
0.694119
0.929197
0.082556
0.827333
0.475806
0.109408
0.355943
0.142326
697
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0.26877
0.3673
21
4
0.890259
1
0.294677
0.139266
0.00426
5.4
13.4
33.9
107.7
407.3
1,651
6,744.9
13,632.5
75
0.761041
0.131833
0.96774
0.160447
0.936196
0.455357
0.035622
0.246694
0.153547
144
1.56199
0.514925
0.5165
425
13
null
null
0.720814
0.519669
0.155237
8
35.2
158.2
755.7
3,510.3
13,168.3
33,269.4
44,543
10
0.771321
0.878469
0.789931
0.128036
0.812444
0.477273
0.117842
0.420503
0.375568
1,071
-1.07425
0.19435
0.3606
36
2
0.820231
39
0.411354
0.343782
0.029171
7.7
30.6
133.9
654.4
3,250.6
13,012.3
32,383.1
42,161.3
75
0.870294
0.219091
0.969962
0.160347
0.911846
0.451613
0.052449
0.30669
0.333188
872
-0.582511
0.44589
0.2909
88
6
0.749981
38
0.528835
0.523448
0.107341
7.8
29.3
114.2
482.1
2,225.2
9,917.3
32,464.7
46,734.9
35
0.709366
1.21492
0.856313
0.059887
0.761119
0.477273
0.115262
0.437703
0.415732
712
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0.536815
0.468
211
12
0.794707
41
0.576435
0.462696
0.07203
7.7
39.9
186.4
862.7
3,225.5
8,466.1
17,373.7
24,504.6
75
0.767839
0.526376
0.904605
0.124788
0.872822
0.46988
0.074922
0.356194
0.224235
1,353
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0.2094
98
4
0.816815
22
0.399101
0.332413
0.045445
2
4
8
16
32
63.9
127.5
254.3
65
0.778492
0.354509
0.961044
0.10025
0.903893
0.455959
0.038267
0.220367
0
16
1.32889
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0.2361
12
2
0.914267
5
0.171319
0.090028
0.003628
6.5
15.6
42.7
131.1
428.8
1,317
3,859.5
6,980.4
75
0.780511
0.046814
0.987524
0.210969
0.981423
0.439394
0.004192
0.135794
0.28596
178
1.82017
0.662698
0.751
140
7
0.84163
18
0.622131
0.288564
0.118781
8
41.5
183.4
762
2,674.5
7,121.2
15,955.1
23,785.8
85
0.696297
0.332587
0.882672
0.269933
0.906339
0.469697
0.049339
0.297355
0.183169
1,221
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0.904829
0.1625
70
8
0.860543
17
0.396625
0.228783
0.048982
7.4
21.4
54.7
168.6
549.7
1,798.6
5,200.6
9,110.6
65
0.816295
0.212721
0.968338
0.178885
0.946895
0.442424
0.027717
0.188362
0.069776
230
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0.368111
0.7221
88
4
0.803646
40
0.457484
0.383981
0.036638
8
41.1
208
929.8
3,367.1
9,029.2
18,163.6
23,805.9
85
0.742826
0.509843
0.951565
0.152279
0.888973
0.465625
0.050867
0.295606
0.34849
1,346
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0.987095
0.5527
266
12
null
null
0.681714
0.421874
0.075532
7.2
29.2
105.7
454.1
1,990.8
8,013.7
24,917.1
37,116.5
35
0.770652
0.517004
0.94477
0.093659
0.89185
0.460606
0.075255
0.310753
0.477566
599
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0.191333
0.2542
191
9
0.748148
53
0.571392
0.596459
0.130823
8
39.7
188.8
921.9
4,235.4
14,937.2
34,475.2
45,911.4
25
0.738121
0.94322
0.897856
0.062223
0.803275
0.475758
0.121562
0.418194
0.356924
1,491
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0.047812
0.0614
11
1
0.899804
2
0.16557
0.041577
0.0021
7.3
11
19.9
58.4
188.4
503.4
1,168
2,189.8
45
0.833824
0.005104
0.89273
0.975704
0.997561
0.439024
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29,834.3
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175
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0.038299
8
43.7
217.3
966.5
3,495.1
9,875.9
19,200.3
25,105.6
35
0.678778
0.458731
0.900764
0.236804
0.888763
0.473118
0.054859
0.311003
0.324323
1,527
-1.02806
0.148433
0.4349
86
7
0.808172
29
0.46383
0.364857
0.035671
8
45
202.5
843.3
2,933.1
7,770.1
16,408.4
22,771.2
75
0.798994
0.466734
0.930661
0.120849
0.877701
0.467456
0.043235
0.320951
0.420515
1,250
-0.51252
0.751362
0.3014
41
3
0.823675
30
0.397978
0.322681
0.041221
2
4
8
16
32
64
127.9
255.4
65
0.760455
0.192828
0.976401
0.116376
0.923556
0.413979
0.0272
0.20648
0
16
1.46592
0.827612
0.7003
189
10
0.734465
56
0.633657
0.501555
0.108704
8
44.7
236.7
1,231.7
5,620
18,050.6
36,868.5
46,191.1
75
0.695087
0.638969
0.959521
0.177868
0.880108
0.454301
0.072421
0.361019
0.417571
1,763
-0.727572
0.557266
0.5515
139
7
0.841126
35
0.439598
0.301306
0.031764
7.7
34.9
136.2
555.4
2,072.4
6,806.1
17,430.6
24,602.7
85
0.773133
0.244224
0.958584
0.349888
0.92697
0.435484
0.040717
0.24351
0.192822
765
-0.070529
0.599826
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Surprising Visual Genome (SVG)

This repository contains the dataset used in the paper "Complexity in Complexity: Understanding Visual Complexity Through Structure, Color, and Surprise". The dataset includes visual complexity ratings along with various features that help predict perceived visual complexity.

Dataset Description

The dataset includes the following features:

Column Description
image_id Unique identifier for each image
complexity Human-rated visual complexity score
# of SAM segmentations Number of segments identified by SAM model
# of FC-CLIP classes Number of semantic classes identified by FC-CLIP
symmetry Measure of image patch symmetry
clutter Measure of visual clutter
IC9600 Predicted complexity using IC9600
MSG Multi-Scale Sobel Gradient score
edge density Traditional edge density measurement
MUC1-MUC8 Multi-Scale Unique Color scores at different bit precisions (1-8)
gemini surprise score Surprise score generated by Gemini-1.5-flash LLM
human surprise score Human-rated surprise score
AMNet memorability Memorability prediction scores using AMNet
M1-M11 Previous handcrafted features (by Corchs et al.)

Features Explained

  • Multi-Scale Sobel Gradient (MSG): Captures local symmetry across multiple scales using a 5x5 Sobel operator, quantifying image structure
  • Multi-Scale Unique Color (MUC): Quantifies color diversity at multiple scales and different color resolutions
  • Surprise Scores: Measures how unexpected or novel the image content is, generated using Gemini-1.5-flash LLM

Usage

This dataset can be used to:

  1. Study and predict visual complexity
  2. Analyze the relationship between visual features and perceived complexity
  3. Develop and test new complexity prediction models
  4. Investigate the contribution of surprise to visual complexity

Citation

If you use this dataset in your research, please cite:

@misc{sarıtaş2025complexitycomplexityunderstandingvisual,
      title={Complexity in Complexity: Understanding Visual Complexity Through Structure, Color, and Surprise}, 
      author={Karahan Sarıtaş and Peter Dayan and Tingke Shen and Surabhi S Nath},
      year={2025},
      eprint={2501.15890},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2501.15890}, 
}

Links

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