Personalized Geo-Specific Tag Recommendation for Photos on Social Websites
文献类型：期刊论文
| 作者 | Liu, Jing1 ; Li, Zechao2; Tang, Jinhui2; Jiang, Yu1; Lu, Hanqing1
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| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
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| 出版日期 | 2014-04-01 |
| 卷号 | 16期号:3页码:588-600 |
| 关键词 | Geo-location preference personalized tag recommendation subspace learning tagging history user preference |
| 英文摘要 | Social 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
| 研究领域[WOS] | Computer Science ; Telecommunications |
| 关键词[WOS] | IMAGE ANNOTATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000333111500002 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3347]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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