Learning multi-level representations for image emotion recognition in the deep convolutional network

Hao Zhang; Yanan Liu; Dan Xu; Kangjian He; Guoqing Peng; Yingying Yue; Ruhan Liu

doi:10.1117/12.2623414

16 February 2022 Learning multi-level representations for image emotion recognition in the deep convolutional network

Hao Zhang, Yanan Liu, Dan Xu, Kangjian He, Guoqing Peng, Yingying Yue, Ruhan Liu

Author Affiliations +

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 1208321 (2022) https://doi.org/10.1117/12.2623414
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

Images can convey rich semantic information and arouse strong emotions in the viewer. With the growing trend of online images and videos to express opinions, evaluating emotions from visual content has attracted considerable attention. Image emotion recognition aims to classify the emotions conveyed by images automatically. The existing image sentiment classification studies using manual features or deep models mainly focus on low-level visual features or high-level semantic representation without considering all factors. In this paper, we adopt visualization to study the working principle of deep representation in emotion recognition. Research shows that the deep model mainly relies on deep semantic information while ignoring the features of shallow visual details, which are essential to evoke emotions. To form a more discriminative representation for emotion recognition, we propose a multi-level representation model with side branches that learns and integrates different depth representations of the backbone for sentiment analysis. Unlike the hierarchy CNN structure, our model provides a description from the deep semantic representation to shallow visual representation. Additionally, several feature fusion approaches are analyzed and discussed to optimize the deep model. Extensive experiments on several image emotion recognition datasets show that our model outperforms various existing methods.

Citation Download Citation

Hao Zhang, Yanan Liu, Dan Xu, Kangjian He, Guoqing Peng, Yingying Yue, and Ruhan Liu "Learning multi-level representations for image emotion recognition in the deep convolutional network", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 1208321 (16 February 2022); https://doi.org/10.1117/12.2623414

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KEYWORDS

Visualization

Image analysis

Data modeling

Image classification

Feature extraction

Classification systems

Artificial intelligence

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