Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions

doi:10.1109/TMM.2018.2803520

CASIA OpenIR > 模式识别实验室

	Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions
	Yang, Jufeng 1; She, Dongyu 1; Sun, Ming 1; Cheng, Ming-Ming 1; Rosin, Paul L.2; Wang, Liang3
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
	2018-09-01
卷号	20 期号:9 页码:2513-2525
文章类型	Article
摘要	Automatic 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.
关键词	Affective Region Convolutional Neural Networks Sentiment Classification Visual Sentiment Analysis
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TMM.2018.2803520
关键词[WOS]	OBJECT DETECTION ; NETWORKS ; SEGMENTATION ; IMAGES
收录类别	SCI
语种	英语
项目资助者	National 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研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000442358200022
引用统计	被引频次：123[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/21872
专题	模式识别实验室
作者单位	1.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
推荐引用方式 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.