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Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions
Yang, Jufeng1; She, Dongyu1; Sun, Ming1; Cheng, Ming-Ming1; Rosin, Paul L.2; Wang, Liang3
AbstractAutomatic assessment of sentiment from visual content has gained considerable attention with the increasing tendency of expressing opinions via images and videos online. This paper investigates the problem of visual sentiment analysis, which involves a high-level abstraction in the recognition process. While most of the current methods focus on improving holistic representations, we aim to utilize the local information, which is inspired by the observation that both the whole image and local regions convey significant sentiment information. We propose a framework to leverage affective regions, where we first use an off-the-shelf objectness tool to generate the candidates, and employ a candidate selection method to remove redundant and noisy proposals. Then, a convolutional neural network (CNN) is connected with each candidate to compute the sentiment scores, and the affective regions are automatically discovered, taking the objectness score as well as the sentiment score into consideration. Finally, the CNN outputs from local regions are aggregated with the whole images to produce the final predictions. Our framework only requires image-level labels, thereby significantly reducing the annotation burden otherwise required for training. This is especially important for sentiment analysis since sentiment can be abstract, and labeling affective regions is too subjective and labor-consuming. Extensive experiments show that the proposed algorithm outperforms the state-of-the-art approaches on eight popular benchmark datasets.
KeywordAffective Region Convolutional Neural Networks Sentiment Classification Visual Sentiment Analysis
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61620106008 ; Open Project Program of the National Laboratory of Pattern Recognition ; Huawei Innovation Research Program ; CAST YESS Program ; IBM Global SUR award ; 61572264 ; 61633021 ; 61525306 ; 61301238 ; 61201424)
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000442358200022
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Document Type期刊论文
Affiliation1.Nankai Univ, Sch Comp Sci & Control Engn, Tianjin 300350, Peoples R China
2.Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF10 3AT, S Glam, Wales
3.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yang, Jufeng,She, Dongyu,Sun, Ming,et al. Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2018,20(9):2513-2525.
APA Yang, Jufeng,She, Dongyu,Sun, Ming,Cheng, Ming-Ming,Rosin, Paul L.,&Wang, Liang.(2018).Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions.IEEE TRANSACTIONS ON MULTIMEDIA,20(9),2513-2525.
MLA Yang, Jufeng,et al."Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions".IEEE TRANSACTIONS ON MULTIMEDIA 20.9(2018):2513-2525.
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