Signal Processing - July 2017 - 104

Table 9. The performance on automatic image cropping.
Previous Work

*Photographer 1

Photographer 2

Photographer 3

Park et al. [111]

0.6034 (0.1062)

0.5823 (0.1128)

0.6085 (0.1102)

Yan et al. [108]

0.7487 (0.0667)

0.7288 (0.0720)

0.7322 (0.0719)

Wang et al. [112]

0.7823 (0.0623)

0.7697 (0.0617)

0.7725 (0.0701)

Yan et al. [107]

0.7974 (0.0528)

0.7857 (0.0567)

0.7723 (0.0594)

Vanilla VGG-16 (ImageNet)

0.6971 (0.0580)

0.6841 (0.0618)

0.6715 (0.0613)

DAN-1 (original) (AVA training partition)

0.7637 (0.0437)

0.7437 (0.0493)

0.7360 (0.0495)

DAN-1 (regression) (cropping data fine-tuned)

0.8059 (0.0310)

0.7750 (0.0375)

0.7725 (0.0377)

Proposed Baselines

*There are separate ground-truth annotations by three different photographers in the cropping data set of [107].
The first number is the average overlap ratio (higher is better). The second number (shown in parentheses) is the average boundary displacement error (lower is better).
Bold values signify the best performance by the corresponding methods.

Conclusion and potential directions
Models with competitive performance on image aesthetic
assessment have been seen in the literature, yet the state of
research in this field is far from saturated. Challenging issues
include the ground-truth ambiguity due to neutral image aesthetics and how to effectively learn category-specific image
aesthetics from the limited amount of auxiliary data information. Image aesthetic assessment can also benefit from an even
larger volume of data, with richer annotations, where every
single image is labeled by more users with diverse backgrounds. A large and more diverse data set will facilitate the
learning of future models and potentially allow more
meaningful statistics to be captured.
In this work, we systematically review major attempts on
image aesthetic assessment in the literature and further propose an alternative baseline to investigate the challenging
problem of understanding image aesthetics. We also discuss
an extension of image aesthetic assessment to the application
of automatic image cropping by adapting the learned aestheticclassification CNN for the task of aesthetics-based image cropping. We hope that this survey can serve as a comprehensive
reference source and inspire future research in understanding
image aesthetics and fostering many potential applications.

Authors
Yubin Deng (dy015@ie.cuhk.edu.hk) received his B.Eng.
degree (first-class honors) in information engineering from the
Chinese University of Hong Kong in 2015. He is currently
working toward his Ph.D. degree in the Department of
Information Engineering, Chinese University of Hong Kong,
with a Hong Kong Ph.D. Fellowship. His research interests
include computer vision, pattern recognition, and machine
learning. He was a Hong Kong Jockey Club Scholar in 2013-
2014. He received the Professor Charles K. Kao Student
Creativity Awards champion award in 2015.
Chen Change Loy (ccloy@ie.cuhk.edu.hk) received his
B.Eng degree (first-class honors) from the University of
104

Science, Malaysia, in 2005 and his Ph.D. degree in computer
science from Queen Mary University of London, United
Kingdom, in 2010. He is currently a research assistant professor in the Department of Information Engineering, Chinese
University of Hong Kong. Previously, he was a postdoctoral
researcher at Queen Mary University of London and Vision
Semantics Ltd. His research interests include computer vision
and pattern recognition, with a focus on facial analysis, deep
learning, and visual surveillance. He serves as an associate
editor of IET Computer Vision Journal and is a guest editor of
Computer Vision and Image Understanding. He is a Member
of the IEEE.
Xiaoou Tang (xtang@ie.cuhk.edu.hk) received his B.S.
degree from the University of Science and Technology of
China, Hefei, in 1990, his M.S. degree from the University of
Rochester, New York, in 1991, and his Ph.D. degree from the
Massachusetts Institute of Technology, Cambridge, in 1996.
He is a professor in and the chair of the Department of
Information Engineering, Chinese University of Hong Kong.
He worked as the group manager of the Visual Computing
Group at Microsoft Research Asia from 2005 to 2008. His
research interests include computer vision, pattern recognition, and video processing. He received the Best Paper Award
at the IEEE Conference on Computer Vision and Pattern
Recognition 2009. He was a program chair of the IEEE
International Conference on Computer Vision 2009, and he is
an editor-in-chief of International Journal of Computer
Vision and an associate editor of IEEE Transactions on
Pattern Analysis and Machine Intelligence. He is a Fellow of
the IEEE.

References

[1] M. Freeman, The Complete Guide to Light and Lighting in Digital
Photography (A Lark Photography Book). New York: Sterling Publishing
Company, 2007.
[2] J. Itten, Design and Form: The Basic Course at the Bauhaus and Later. New
York: Wiley, 1975.
[3] B. London and J. Upton, Photography. London: Pearson, 2005.

IEEE SIGNAL PROCESSING MAGAZINE

|

July 2017

|



Table of Contents for the Digital Edition of Signal Processing - July 2017

Signal Processing - July 2017 - Cover1
Signal Processing - July 2017 - Cover2
Signal Processing - July 2017 - 1
Signal Processing - July 2017 - 2
Signal Processing - July 2017 - 3
Signal Processing - July 2017 - 4
Signal Processing - July 2017 - 5
Signal Processing - July 2017 - 6
Signal Processing - July 2017 - 7
Signal Processing - July 2017 - 8
Signal Processing - July 2017 - 9
Signal Processing - July 2017 - 10
Signal Processing - July 2017 - 11
Signal Processing - July 2017 - 12
Signal Processing - July 2017 - 13
Signal Processing - July 2017 - 14
Signal Processing - July 2017 - 15
Signal Processing - July 2017 - 16
Signal Processing - July 2017 - 17
Signal Processing - July 2017 - 18
Signal Processing - July 2017 - 19
Signal Processing - July 2017 - 20
Signal Processing - July 2017 - 21
Signal Processing - July 2017 - 22
Signal Processing - July 2017 - 23
Signal Processing - July 2017 - 24
Signal Processing - July 2017 - 25
Signal Processing - July 2017 - 26
Signal Processing - July 2017 - 27
Signal Processing - July 2017 - 28
Signal Processing - July 2017 - 29
Signal Processing - July 2017 - 30
Signal Processing - July 2017 - 31
Signal Processing - July 2017 - 32
Signal Processing - July 2017 - 33
Signal Processing - July 2017 - 34
Signal Processing - July 2017 - 35
Signal Processing - July 2017 - 36
Signal Processing - July 2017 - 37
Signal Processing - July 2017 - 38
Signal Processing - July 2017 - 39
Signal Processing - July 2017 - 40
Signal Processing - July 2017 - 41
Signal Processing - July 2017 - 42
Signal Processing - July 2017 - 43
Signal Processing - July 2017 - 44
Signal Processing - July 2017 - 45
Signal Processing - July 2017 - 46
Signal Processing - July 2017 - 47
Signal Processing - July 2017 - 48
Signal Processing - July 2017 - 49
Signal Processing - July 2017 - 50
Signal Processing - July 2017 - 51
Signal Processing - July 2017 - 52
Signal Processing - July 2017 - 53
Signal Processing - July 2017 - 54
Signal Processing - July 2017 - 55
Signal Processing - July 2017 - 56
Signal Processing - July 2017 - 57
Signal Processing - July 2017 - 58
Signal Processing - July 2017 - 59
Signal Processing - July 2017 - 60
Signal Processing - July 2017 - 61
Signal Processing - July 2017 - 62
Signal Processing - July 2017 - 63
Signal Processing - July 2017 - 64
Signal Processing - July 2017 - 65
Signal Processing - July 2017 - 66
Signal Processing - July 2017 - 67
Signal Processing - July 2017 - 68
Signal Processing - July 2017 - 69
Signal Processing - July 2017 - 70
Signal Processing - July 2017 - 71
Signal Processing - July 2017 - 72
Signal Processing - July 2017 - 73
Signal Processing - July 2017 - 74
Signal Processing - July 2017 - 75
Signal Processing - July 2017 - 76
Signal Processing - July 2017 - 77
Signal Processing - July 2017 - 78
Signal Processing - July 2017 - 79
Signal Processing - July 2017 - 80
Signal Processing - July 2017 - 81
Signal Processing - July 2017 - 82
Signal Processing - July 2017 - 83
Signal Processing - July 2017 - 84
Signal Processing - July 2017 - 85
Signal Processing - July 2017 - 86
Signal Processing - July 2017 - 87
Signal Processing - July 2017 - 88
Signal Processing - July 2017 - 89
Signal Processing - July 2017 - 90
Signal Processing - July 2017 - 91
Signal Processing - July 2017 - 92
Signal Processing - July 2017 - 93
Signal Processing - July 2017 - 94
Signal Processing - July 2017 - 95
Signal Processing - July 2017 - 96
Signal Processing - July 2017 - 97
Signal Processing - July 2017 - 98
Signal Processing - July 2017 - 99
Signal Processing - July 2017 - 100
Signal Processing - July 2017 - 101
Signal Processing - July 2017 - 102
Signal Processing - July 2017 - 103
Signal Processing - July 2017 - 104
Signal Processing - July 2017 - 105
Signal Processing - July 2017 - 106
Signal Processing - July 2017 - 107
Signal Processing - July 2017 - 108
Signal Processing - July 2017 - 109
Signal Processing - July 2017 - 110
Signal Processing - July 2017 - 111
Signal Processing - July 2017 - 112
Signal Processing - July 2017 - 113
Signal Processing - July 2017 - 114
Signal Processing - July 2017 - 115
Signal Processing - July 2017 - 116
Signal Processing - July 2017 - 117
Signal Processing - July 2017 - 118
Signal Processing - July 2017 - 119
Signal Processing - July 2017 - 120
Signal Processing - July 2017 - 121
Signal Processing - July 2017 - 122
Signal Processing - July 2017 - 123
Signal Processing - July 2017 - 124
Signal Processing - July 2017 - 125
Signal Processing - July 2017 - 126
Signal Processing - July 2017 - 127
Signal Processing - July 2017 - 128
Signal Processing - July 2017 - 129
Signal Processing - July 2017 - 130
Signal Processing - July 2017 - 131
Signal Processing - July 2017 - 132
Signal Processing - July 2017 - 133
Signal Processing - July 2017 - 134
Signal Processing - July 2017 - 135
Signal Processing - July 2017 - 136
Signal Processing - July 2017 - 137
Signal Processing - July 2017 - 138
Signal Processing - July 2017 - 139
Signal Processing - July 2017 - 140
Signal Processing - July 2017 - 141
Signal Processing - July 2017 - 142
Signal Processing - July 2017 - 143
Signal Processing - July 2017 - 144
Signal Processing - July 2017 - 145
Signal Processing - July 2017 - 146
Signal Processing - July 2017 - 147
Signal Processing - July 2017 - 148
Signal Processing - July 2017 - 149
Signal Processing - July 2017 - 150
Signal Processing - July 2017 - 151
Signal Processing - July 2017 - 152
Signal Processing - July 2017 - 153
Signal Processing - July 2017 - 154
Signal Processing - July 2017 - 155
Signal Processing - July 2017 - 156
Signal Processing - July 2017 - 157
Signal Processing - July 2017 - 158
Signal Processing - July 2017 - 159
Signal Processing - July 2017 - 160
Signal Processing - July 2017 - 161
Signal Processing - July 2017 - 162
Signal Processing - July 2017 - 163
Signal Processing - July 2017 - 164
Signal Processing - July 2017 - 165
Signal Processing - July 2017 - 166
Signal Processing - July 2017 - 167
Signal Processing - July 2017 - 168
Signal Processing - July 2017 - 169
Signal Processing - July 2017 - 170
Signal Processing - July 2017 - 171
Signal Processing - July 2017 - 172
Signal Processing - July 2017 - 173
Signal Processing - July 2017 - 174
Signal Processing - July 2017 - 175
Signal Processing - July 2017 - 176
Signal Processing - July 2017 - 177
Signal Processing - July 2017 - 178
Signal Processing - July 2017 - 179
Signal Processing - July 2017 - 180
Signal Processing - July 2017 - 181
Signal Processing - July 2017 - 182
Signal Processing - July 2017 - 183
Signal Processing - July 2017 - 184
Signal Processing - July 2017 - 185
Signal Processing - July 2017 - 186
Signal Processing - July 2017 - 187
Signal Processing - July 2017 - 188
Signal Processing - July 2017 - 189
Signal Processing - July 2017 - 190
Signal Processing - July 2017 - 191
Signal Processing - July 2017 - 192
Signal Processing - July 2017 - 193
Signal Processing - July 2017 - 194
Signal Processing - July 2017 - 195
Signal Processing - July 2017 - 196
Signal Processing - July 2017 - Cover3
Signal Processing - July 2017 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201809
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201807
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201805
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201803
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201801
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1117
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0917
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0717
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0517
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0317
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0117
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1116
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0916
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0716
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0516
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0316
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0116
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1115
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0915
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0715
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0515
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0315
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0115
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1114
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0914
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0714
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0514
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0314
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0114
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1113
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0913
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0713
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0513
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0313
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0113
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1112
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0912
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0712
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0512
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0312
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0112
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1111
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0911
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0711
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0511
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0311
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0111
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1110
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0910
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0710
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0510
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0310
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0110
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1109
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0909
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0709
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0509
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0309
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0109
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1108
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0908
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0708
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0508
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0308
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0108
https://www.nxtbookmedia.com