Affective Image Content Analysis: A Comprehensive Survey
Abstract
Images can convey rich semantics and induce strong emotions in viewers. Recently, with the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this paper, we review the state-of-the-art methods comprehensively with respect to two main challenges -- affective gap and perception subjectivity. We begin with an introduction to the key emotion representation models that have been widely employed in AICA. Available existing datasets for performing evaluation are briefly described. We then summarize and compare the representative approaches on emotion feature extraction, personalized emotion prediction, and emotion distribution learning. Finally, we discuss some future research directions.
Cite
Text
Zhao et al. "Affective Image Content Analysis: A Comprehensive Survey." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/780Markdown
[Zhao et al. "Affective Image Content Analysis: A Comprehensive Survey." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/zhao2018ijcai-affective/) doi:10.24963/IJCAI.2018/780BibTeX
@inproceedings{zhao2018ijcai-affective,
title = {{Affective Image Content Analysis: A Comprehensive Survey}},
author = {Zhao, Sicheng and Ding, Guiguang and Huang, Qingming and Chua, Tat-Seng and Schuller, Björn W. and Keutzer, Kurt},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {5534-5541},
doi = {10.24963/IJCAI.2018/780},
url = {https://mlanthology.org/ijcai/2018/zhao2018ijcai-affective/}
}