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Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search
Gao, Yue1; Wang, Meng2; Zha, Zheng-Jun3; Shen, Jialie4; Li, Xuelong5; Wu, Xindong6,7
AbstractDue to the popularity of social media websites, extensive research efforts have been dedicated to tag-based social image search. Both visual information and tags have been investigated in the research field. However, most existing methods use tags and visual characteristics either separately or sequentially in order to estimate the relevance of images. In this paper, we propose an approach that simultaneously utilizes both visual and textual information to estimate the relevance of user tagged images. The relevance estimation is determined with a hypergraph learning approach. In this method, a social image hypergraph is constructed, where vertices represent images and hyperedges represent visual or textual terms. Learning is achieved with use of a set of pseudo-positive images, where the weights of hyperedges are updated throughout the learning process. In this way, the impact of different tags and visual words can be automatically modulated. Comparative results of the experiments conducted on a dataset including 370+ images are presented, which demonstrate the effectiveness of the proposed approach.
KeywordHypergraph Learning Social Image Search Tag Visual-textual
WOS HeadingsScience & Technology ; Technology
Indexed BySCI ; EI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000312892000029
Citation statistics
Cited Times:301[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
2.Hefei Univ Technol, Comp Sci & Informat Engn Dept, Hefei 230009, Peoples R China
3.Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore
4.Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore
5.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Beijing 100864, Peoples R China
6.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China
7.Univ Vermont, Dept Comp Sci, Burlington, VT 05405 USA
Recommended Citation
GB/T 7714
Gao, Yue,Wang, Meng,Zha, Zheng-Jun,et al. Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2013,22(1):363-376.
APA Gao, Yue,Wang, Meng,Zha, Zheng-Jun,Shen, Jialie,Li, Xuelong,&Wu, Xindong.(2013).Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search.IEEE TRANSACTIONS ON IMAGE PROCESSING,22(1),363-376.
MLA Gao, Yue,et al."Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search".IEEE TRANSACTIONS ON IMAGE PROCESSING 22.1(2013):363-376.
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