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A Hierarchical CNN-RNN Approach for Visual Emotion Classification

文献类型：期刊论文

作者Li, Liang1; Zhu, Xinge2; Hao, Yiming3; Wang, Shuhui1; Gao, Xingyu4,6; Huang, Qingming2,4,5
刊名ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
出版日期2019
卷号15期号:3页码:17
关键词Visual emotion recognition multi-task learning feature fusing hierarchical CNN-RNN stacked bi-directional RNN
ISSN号1551-6857
DOI10.1145/3359753
英文摘要Visual emotion classification is predicting emotional reactions of people for the given visual content. Psychological studies show that human emotions are affected by various visual stimuli from low level to high level, including contrast, color, texture, scene, object, and association, among others. Traditional approaches regarded different levels of stimuli as independent components and ignored to effectively fuse different stimuli. This article proposes a hierarchical convolutional neural network (CNN)-recurrent neural network (RNN) approach to predict the emotion based on the fused stimuli by exploiting the dependency among different-level features. First, we introduce a dual CNN to extract different levels of visual stimulus, where two related loss functions are designed to learn the stimuli representation under a multi-task learning structure. Further, to model the dependency between the low- and high-level stimulus, a stacked bi-directional RNN is proposed to fuse the preceding learned features from the dual CNN. Comparison experiments on one large-scale and three small scale datasets show that the proposed approach brings significant improvement. Ablation experiments demonstrate the effectiveness of different modules from our model.
资助项目National MCF Energy RD Program[2018YFE0303100] ; National Natural Science Foundation of China[61771457] ; National Natural Science Foundation of China[61732007] ; National Natural Science Foundation of China[61772494] ; National Natural Science Foundation of China[61672497] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[61472389] ; National Natural Science Foundation of China[61702491] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[U1636214] ; Key Research Program of Frontier Sciences, Chinese Academy of Sciences[QYZDJ-SSWSYS013]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000535718800013
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/15314]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Shuhui
作者单位1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, CAS, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, 19 A Yuquan Rd, Beijing 100049, Peoples R China
3.Shandong Univ, Shanda South Rd 27, Jinan 250100, Peoples R China
4.Chinese Acad Sci, Beijing, Peoples R China
5.Peng Cheng Lab, Shenzhen, Peoples R China
6.Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China
推荐引用方式
GB/T 7714		 Li, Liang,Zhu, Xinge,Hao, Yiming,et al. A Hierarchical CNN-RNN Approach for Visual Emotion Classification[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2019,15(3):17.
APA Li, Liang,Zhu, Xinge,Hao, Yiming,Wang, Shuhui,Gao, Xingyu,&Huang, Qingming.(2019).A Hierarchical CNN-RNN Approach for Visual Emotion Classification.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,15(3),17.
MLA Li, Liang,et al."A Hierarchical CNN-RNN Approach for Visual Emotion Classification".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 15.3(2019):17.

入库方式： OAI收割

来源：计算技术研究所

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