文献类型：期刊文献

英文题名：Object semantics sentiment correlation analysis enhanced image sentiment classification

作者：Zhang, Jing[1];Chen, Mei[1];Sun, Han[1];Li, Dongdong[1];Wang, Zhe[1]

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

年份：2020

卷号：191

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20195107875528);WOS:【SCI-EXPANDED(收录号:WOS:000517663200027)】；

基金：This research has been supported by the National Nature Sci-ence Foundation of China (Grant 61672227 and Grant 61806078).

语种：英文

外文关键词：Image sentiment classification; Object semantics; Bayesian network; Object semantics sentiment correlation model; Convolutional Neural Network

摘要：With the development of artificial intelligence and deep learning, image sentiment analysis has become a hotspot in computer vision and attracts more attention. Most of the existing methods focus on identifying the emotions by studying complex models or robust features from the whole image, which neglects the influence of object semantics on image sentiment analysis. In this paper, we propose a novel object semantics sentiment correlation model (OSSCM), which is based on Bayesian network, to guide the image sentiment classification. OSSCM is constructed by exploring the relationships between image emotions and the object semantics combination in the images, which can fully consider the effect of object semantics for image emotions. Then, a convolutional neural networks (CNN) based visual sentiment analysis model is proposed to analyze image sentiment from visual aspect. Finally, three fusion strategies are proposed to realize OSSCM enhanced image sentiment classification. Experiments on public emotion datasets Fl and Flickr_LDL, demonstrate that our proposed image sentiment classification method can achieve good performance on image emotion analysis, and outperform state of the art methods. (C) 2019 Elsevier B.V. All rights reserved.