Affective image content analysis: A comprehensive survey

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

66 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
EditorsJerome Lang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages5534-5541
Number of pages8
ISBN (Electronic)9780999241127
DOIs
StatePublished - 2018
Externally publishedYes
Event27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, Sweden
Duration: 13 Jul 201819 Jul 2018

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2018-July
ISSN (Print)1045-0823

Conference

Conference27th International Joint Conference on Artificial Intelligence, IJCAI 2018
Country/TerritorySweden
CityStockholm
Period13/07/1819/07/18

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