CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Personalized Geo-Specific Tag Recommendation for Photos on Social Websites
Liu, Jing1; Li, Zechao2; Tang, Jinhui2; Jiang, Yu1; Lu, Hanqing1
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
2014-04-01
Volume16Issue:3Pages:588-600
SubtypeArticle
AbstractSocial tagging becomes increasingly important to organize and search large-scale community-contributed photos on social websites. To facilitate generating high-quality social tags, tag recommendation by automatically assigning relevant tags to photos draws particular research interest. In this paper, we focus on the personalized tag recommendation task and try to identify user-preferred, geo-location-specific as well as semantically relevant tags for a photo by leveraging rich contexts of the freely available community-contributed photos. For users and geo-locations, we assume they have different preferred tags assigned to a photo, and propose a subspace learning method to individually uncover the both types of preferences. The goal of our work is to learn a unified subspace shared by the visual and textual domains to make visual features and textual information of photos comparable. Considering the visual feature is a lower level representation on semantics than the textual information, we adopt a progressive learning strategy by additionally introducing an intermediate subspace for the visual domain, and expect it to have consistent local structure with the textual space. Accordingly, the unified subspace is mapped from the intermediate subspace and the textual space respectively. We formulate the above learning problems into a united form, and present an iterative optimization with its convergence proof. Given an untagged photo with its geo-location to a user, the user-preferred and the geo-location-specific tags are found by the nearest neighbor search in the corresponding unified spaces. Then we combine the obtained tags and the visual appearance of the photo to discover the semantically and visually related photos, among which the most frequent tags are used as the recommended tags. Experiments on a large-scale data set collected from Flickr verify the effectivity of the proposed solution.
KeywordGeo-location Preference Personalized Tag Recommendation Subspace Learning Tagging History User Preference
WOS HeadingsScience & Technology ; Technology
WOS KeywordIMAGE ANNOTATION
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000333111500002
Citation statistics
Cited Times:29[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3347
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing 210094, Jiangsu, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Liu, Jing,Li, Zechao,Tang, Jinhui,et al. Personalized Geo-Specific Tag Recommendation for Photos on Social Websites[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2014,16(3):588-600.
APA Liu, Jing,Li, Zechao,Tang, Jinhui,Jiang, Yu,&Lu, Hanqing.(2014).Personalized Geo-Specific Tag Recommendation for Photos on Social Websites.IEEE TRANSACTIONS ON MULTIMEDIA,16(3),588-600.
MLA Liu, Jing,et al."Personalized Geo-Specific Tag Recommendation for Photos on Social Websites".IEEE TRANSACTIONS ON MULTIMEDIA 16.3(2014):588-600.
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