{"type":"ScholarlyArticle","title":"Affective Image Content Analysis: A Comprehensive Survey","authors":[{"name":"Sicheng Zhao","slug":"sicheng-zhao","given":"Sicheng","family":"Zhao","affiliation":"School of Software , Tsinghua University , China"},{"name":"Guiguang Ding","slug":"guiguang-ding","given":"Guiguang","family":"Ding","affiliation":"‡ Department of Electrical Engineering and Computer Sciences , University of California , Berkeley , USA"},{"name":"Qingming Huang","slug":"qingming-huang","given":"Qingming","family":"Huang","affiliation":"School of Software , Tsinghua University , China"},{"name":"Tat-Seng Chua","slug":"tat-seng-chua","given":"Tat-Seng","family":"Chua","affiliation":"§ School of Computer and Control Engineering , University of Chinese Academy of Sciences , China"},{"name":"Björn W. Schuller","slug":"bjorn-w-schuller","given":"Björn W.","family":"Schuller","affiliation":"♦ Department of Computing , Imperial College London , UK; School of Computing , National University of Singapore , Singapore"},{"name":"Kurt Keutzer","slug":"kurt-keutzer","given":"Kurt","family":"Keutzer","affiliation":"School of Software , Tsinghua University , China"}],"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.","doi":"10.24963/ijcai.2018/780","url":"https://ijcai.org/proceedings/2018/780","pdf":"https://ijcai.org/proceedings/2018/0780.pdf","markdown":"https://ijcai.org/proceedings/2018/780.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/780","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Survey track","track":"Survey track","number":780,"pages":{"first":5534,"last":5541},"keywords":["Uncertainty in AI: Uncertainty in AI","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Humans and AI: Personalization and User Modeling","Multidisciplinary Topics and Applications: AI and the Web"],"videos":[],"references":[{"position":1,"raw":"[Alameda-Pineda et al., 2016] Xavier Alameda-Pineda, Elisa Ric- ci, Yan Yan, and Nicu Sebe. 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