Affective Video Content Analysis: Decade Review and New Perspectives
Video content is rich in semantics and has the ability to evoke various emotions in viewers. In recent years, with the rapid development of affective computing and the explosive growth of visual data, Affective Video Content Analysis (AVCA) as an essential branch of affective computing has become a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2025-02-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020048 |
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| _version_ | 1850169533056155648 |
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| author | Junxiao Xue Jie Wang Xiaozhen Liu Qian Zhang Xuecheng Wu |
| author_facet | Junxiao Xue Jie Wang Xiaozhen Liu Qian Zhang Xuecheng Wu |
| author_sort | Junxiao Xue |
| collection | DOAJ |
| description | Video content is rich in semantics and has the ability to evoke various emotions in viewers. In recent years, with the rapid development of affective computing and the explosive growth of visual data, Affective Video Content Analysis (AVCA) as an essential branch of affective computing has become a widely researched topic. In this study, we comprehensively review the development of AVCA over the past decade, particularly focusing on the most advanced methods adopted to address the three major challenges of video feature extraction, expression subjectivity, and multimodal feature fusion. We first introduce the widely used emotion representation models in AVCA and describe commonly used datasets. We summarize and compare representative methods in the following aspects: (1) unimodal AVCA models, including facial expression recognition and posture emotion recognition; (2) multimodal AVCA models, including feature fusion, decision fusion, and attention-based multimodal models; and (3) model performance evaluation standards. Finally, we discuss future challenges and promising research directions, such as emotion recognition and public opinion analysis, human-computer interaction, and emotional intelligence. |
| format | Article |
| id | doaj-art-a1a9b09492c848d3b62df98f70e0b667 |
| institution | OA Journals |
| issn | 2096-0654 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-a1a9b09492c848d3b62df98f70e0b6672025-08-20T02:20:42ZengTsinghua University PressBig Data Mining and Analytics2096-06542025-02-018111814410.26599/BDMA.2024.9020048Affective Video Content Analysis: Decade Review and New PerspectivesJunxiao Xue0Jie Wang1Xiaozhen Liu2Qian Zhang3Xuecheng Wu4Research Center for Space Computing System, Zhejiang Lab, Hangzhou 311500, ChinaChina Mobile (Hangzhou) Information Technology Co. Ltd., Hangzhou 311100, ChinaSchool of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, ChinaSchool of Computer Science, National University of Defense Technology, Changsha 410073, ChinaSchool of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, ChinaVideo content is rich in semantics and has the ability to evoke various emotions in viewers. In recent years, with the rapid development of affective computing and the explosive growth of visual data, Affective Video Content Analysis (AVCA) as an essential branch of affective computing has become a widely researched topic. In this study, we comprehensively review the development of AVCA over the past decade, particularly focusing on the most advanced methods adopted to address the three major challenges of video feature extraction, expression subjectivity, and multimodal feature fusion. We first introduce the widely used emotion representation models in AVCA and describe commonly used datasets. We summarize and compare representative methods in the following aspects: (1) unimodal AVCA models, including facial expression recognition and posture emotion recognition; (2) multimodal AVCA models, including feature fusion, decision fusion, and attention-based multimodal models; and (3) model performance evaluation standards. Finally, we discuss future challenges and promising research directions, such as emotion recognition and public opinion analysis, human-computer interaction, and emotional intelligence.https://www.sciopen.com/article/10.26599/BDMA.2024.9020048affective computingvideo emotionvideo feature extractionmachine learningemotional intelligence |
| spellingShingle | Junxiao Xue Jie Wang Xiaozhen Liu Qian Zhang Xuecheng Wu Affective Video Content Analysis: Decade Review and New Perspectives Big Data Mining and Analytics affective computing video emotion video feature extraction machine learning emotional intelligence |
| title | Affective Video Content Analysis: Decade Review and New Perspectives |
| title_full | Affective Video Content Analysis: Decade Review and New Perspectives |
| title_fullStr | Affective Video Content Analysis: Decade Review and New Perspectives |
| title_full_unstemmed | Affective Video Content Analysis: Decade Review and New Perspectives |
| title_short | Affective Video Content Analysis: Decade Review and New Perspectives |
| title_sort | affective video content analysis decade review and new perspectives |
| topic | affective computing video emotion video feature extraction machine learning emotional intelligence |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020048 |
| work_keys_str_mv | AT junxiaoxue affectivevideocontentanalysisdecadereviewandnewperspectives AT jiewang affectivevideocontentanalysisdecadereviewandnewperspectives AT xiaozhenliu affectivevideocontentanalysisdecadereviewandnewperspectives AT qianzhang affectivevideocontentanalysisdecadereviewandnewperspectives AT xuechengwu affectivevideocontentanalysisdecadereviewandnewperspectives |