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Previous studies tend to use visual features obtained from a global perspective and ignore the role of local visual features in emotional arousal. Moreover, the CNN shallow feature maps contain image content information; such maps obtained from shallow layers directly to describe low-level visual features may lead to redundancy. In order to enhance image emotion recognition performance, an improved CNN is proposed in this work. Firstly, the saliency detection algorithm is used to locate the emotional region of the image, which is served as the supplementary information to conduct emotion recognition better. Secondly, the Gram matrix transform is performed on the CNN shallow feature maps to decrease the redundancy of image content information. Finally, a new loss function is designed by using hard labels and probability labels of image emotion category to reduce the influence of image emotion subjectivity. Extensive experiments have been conducted on benchmark datasets, including FI (Flickr and Instagram), IAPSsubset, ArtPhoto, and Abstract. The experimental results show that compared with the existing approaches, our method has a good application prospect.<\/jats:p>","DOI":"10.1155\/2021\/6854586","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T03:45:43Z","timestamp":1628653543000},"page":"1-12","source":"Crossref","is-referenced-by-count":6,"title":["A Saliency Detection and Gram Matrix Transform-Based Convolutional Neural Network for Image Emotion Classification"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8518-793X","authenticated-orcid":true,"given":"Zelin","family":"Deng","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6476-5454","authenticated-orcid":true,"given":"Qiran","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China"},{"name":"School of Big Data and Artificial Intelligence, Xinyang University, Xinyang 464000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1099-1525","authenticated-orcid":true,"given":"Pei","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2789-2980","authenticated-orcid":true,"given":"Dengyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8523-4938","authenticated-orcid":true,"given":"Yuansheng","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"first-page":"308","article-title":"Building a large scale dataset for image emotion recognition: the fine print and the benchmark","author":"Q. 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