A Survey on Image Emotion Recognition


Guangzhe Zhao, Hanting Yang, Bing Tu, Lei Zhang, Journal of Information Processing Systems Vol. 17, No. 6, pp. 1138-1156, Dec. 2021  

10.3745/JIPS.01.0082
Keywords: Emotion Semantics, Image Emotion Recognition, Image Feature Extraction, Machine Learning
Fulltext:

Abstract

Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.


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Cite this article
[APA Style]
Guangzhe Zhao, Hanting Yang, Bing Tu, & Lei Zhang (2021). A Survey on Image Emotion Recognition. Journal of Information Processing Systems, 17(6), 1138-1156. DOI: 10.3745/JIPS.01.0082.

[IEEE Style]
G. Zhao, H. Yang, B. Tu and L. Zhang, "A Survey on Image Emotion Recognition," Journal of Information Processing Systems, vol. 17, no. 6, pp. 1138-1156, 2021. DOI: 10.3745/JIPS.01.0082.

[ACM Style]
Guangzhe Zhao, Hanting Yang, Bing Tu, and Lei Zhang. 2021. A Survey on Image Emotion Recognition. Journal of Information Processing Systems, 17, 6, (2021), 1138-1156. DOI: 10.3745/JIPS.01.0082.