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|Title:||Social visual image ranking for web image search|
Social image search
|Source:||Liu, S.,Cui, P.,Luan, H.,Zhu, W.,Yang, S.,Tian, Q. (2013). Social visual image ranking for web image search. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7732 LNCS (PART 1) : 239-249. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-35725-1_22|
|Abstract:||Many research have been focusing on how to match the textual query with visual images and their surrounding texts or tags for Web image search. The returned results are often unsatisfactory due to their deviation from user intentions. In this paper, we propose a novel image ranking approach to web image search, in which we use social data from social media platform jointly with visual data to improve the relevance between returned images and user intentions (i.e., social relevance). Specifically, we propose a community-specific Social-Visual Ranking(SVR) algorithm to rerank the Web images by taking social relevance into account. Through extensive experiments, we demonstrated the importance of both visual factors and social factors, and the effectiveness and superiority of the social-visual ranking algorithm for Web image search. © Springer-Verlag 2013.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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