Affective Image Content Analysis: A Comprehensive Survey

Affective Image Content Analysis: A Comprehensive Survey

Sicheng Zhao, Guiguang Ding, Qingming Huang, Tat-Seng Chua, Björn W. Schuller, Kurt Keutzer

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Survey track. Pages 5534-5541. https://doi.org/10.24963/ijcai.2018/780

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.
Keywords:
Humans and AI: Personalization and User Modeling
Uncertainty in AI: Uncertainty in AI
Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation
Multidisciplinary Topics and Applications: AI and the Web