文献类型：期刊文献

英文题名：Image sentiment classification via multi-level sentiment region correlation analysis

作者：Zhang, Jing[1];Liu, Xinyu[1];Chen, Mei[1];Ye, Qi[1];Wang, Zhe[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2022

卷号：469

起止页码：221

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20214611148192);WOS:【SCI-EXPANDED(收录号:WOS:000719323600006)】；

基金：This research has been supported by Shanghai Science and Technology Program "Distributed small sample learning and small sample generation algorithm and theory research" under Grant No. 20511100600.

语种：英文

外文关键词：Image sentiment classification; Sentiment region; Transformer; Multi-level sentiment region correlation analysis

摘要：Human's understanding of image content is a multi-level and multi-stage process. For visual sentiment analysis, this process can be specified as the gradual perception from semantic to emotion of regions in an image. The mining of emotion-related regions is valuable for sentiment recognition, and it is even more important to further investigate the semantic associations formed between these regions. In this paper, we propose a novel multi-level sentiment region correlation analysis model, which exploits the regions in an image that are most potentially affected by emotions from multiple perspectives and motivates the interaction between sentiment regions. It makes the visual content of multi-level sentiment regions and the implicit correlations within them robust cues for image sentiment recognition. We innovatively propose a module of correlation analysis of multi-level sentiment regions to exploit the effects of higher-order and rich interactions on emotions with encoders of the Transformer. Experiments on a variety of public visual sentiment analysis datasets at different scales show that the proposed MSRCA model achieve excellent performance in image sentiment classification and outperforms other existing methods. (c) 2021 Elsevier B.V. All rights reserved.