TY - GEN
T1 - Intrinsic image popularity assessment
AU - Ding, Keyan
AU - Ma, Kede
AU - Wang, Shiqi
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/10/15
Y1 - 2019/10/15
N2 - The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on the Internet. Here, we aim to single out the contribution of visual content to image popularity, i.e., intrinsic image popularity. Specifically, we first describe a probabilistic method to generate massive popularity-discriminable image pairs, based on which the first large-scale image database for intrinsic IPA (I2PA) is established. We then develop computational models for I2PA based on deep neural networks, optimizing for ranking consistency with millions of popularity-discriminable image pairs. Experiments on Instagram and other social platforms demonstrate that the optimized model performs favorably against existing methods, exhibits reasonable generalizability on different databases, and even surpasses human-level performance on Instagram. In addition, we conduct a psychophysical experiment to analyze various aspects of human behavior in I2PA.
AB - The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on the Internet. Here, we aim to single out the contribution of visual content to image popularity, i.e., intrinsic image popularity. Specifically, we first describe a probabilistic method to generate massive popularity-discriminable image pairs, based on which the first large-scale image database for intrinsic IPA (I2PA) is established. We then develop computational models for I2PA based on deep neural networks, optimizing for ranking consistency with millions of popularity-discriminable image pairs. Experiments on Instagram and other social platforms demonstrate that the optimized model performs favorably against existing methods, exhibits reasonable generalizability on different databases, and even surpasses human-level performance on Instagram. In addition, we conduct a psychophysical experiment to analyze various aspects of human behavior in I2PA.
KW - Deep neural networks
KW - Human behavior analysis
KW - Intrinsic image popularity
KW - Learning-to-rank
UR - https://www.scopus.com/pages/publications/85074808629
U2 - 10.1145/3343031.3351007
DO - 10.1145/3343031.3351007
M3 - 会议稿件
AN - SCOPUS:85074808629
T3 - MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia
SP - 1979
EP - 1987
BT - MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia
PB - Association for Computing Machinery, Inc
T2 - 27th ACM International Conference on Multimedia, MM 2019
Y2 - 21 October 2019 through 25 October 2019
ER -