Tag Quality Improvement for Social Image Hosting Website

• Item type: Trans(1)
• 公開日: 2013-06-28
• タイトル: Tag Quality Improvement for Social Image Hosting Website
• タイトル / 言語: en
• タイトル: Tag Quality Improvement for Social Image Hosting Website
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [研究論文] tag quality, social image hosting websites, graph model
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Department of Social Informatics, Kyoto University
• 著者所属: Department of Social Informatics, Kyoto University
• 著者所属: Department of Social Informatics, Kyoto University
• 著者所属: Department of Social Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Department of Social Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Department of Social Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Department of Social Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Department of Social Informatics, Kyoto University
• 著者名: Jiyi, Li; Qiang, Ma; Yasuhito, Asano; Masatoshi, Yoshikawa
• 著者名(英): Jiyi, Li; Qiang, Ma; Yasuhito, Asano; Masatoshi, Yoshikawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Social image hosting websites such as Flickr provide services to users for sharing their images. Users can upload and tag their images or search for images by using keywords which describe image semantics. However various low quality tags in the user generated folksonomy tags have negative influences on image search results and user experience. To improve tag quality, we propose four approaches with one framework to automatically generate new tags, and rank the new tags as well as the existing raw tags, for both untagged and tagged images. The approaches utilize and integrate both textual and visual information, and analyze intra- and inter- probabilistic relationships among images and tags based on a graph model. The experiments based on the dataset constructed from Flickr illustrate the effectiveness and efficiency of our approaches.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Social image hosting websites such as Flickr provide services to users for sharing their images. Users can upload and tag their images or search for images by using keywords which describe image semantics. However various low quality tags in the user generated folksonomy tags have negative influences on image search results and user experience. To improve tag quality, we propose four approaches with one framework to automatically generate new tags, and rank the new tags as well as the existing raw tags, for both untagged and tagged images. The approaches utilize and integrate both textual and visual information, and analyze intra- and inter- probabilistic relationships among images and tags based on a graph model. The experiments based on the dataset constructed from Flickr illustrate the effectiveness and efficiency of our approaches.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464847
• 書誌情報: 情報処理学会論文誌データベース（TOD）; 巻 6, 号 3, p. 177-186, 発行日 2013-06-28
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7799
• 出版者 / 言語: ja
• 出版者: 情報処理学会