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  1. 060 工学部
  2. 0603 会議発表資料

Image categorization by a classifier based on probabilistic topic model

http://hdl.handle.net/10091/12019
http://hdl.handle.net/10091/12019
4b71ab6c-7fa9-4f92-8dff-fb01a1edccfb
名前 / ファイル ライセンス アクション
icpr2008_final_font.pdf icpr2008_final_font.pdf (405.1 kB)
Item type 会議発表論文 / Conference Paper(1)
公開日 2011-03-10
タイトル
言語 en
タイトル Image categorization by a classifier based on probabilistic topic model
言語
言語 eng
資源タイプ
資源 http://purl.org/coar/resource_type/c_5794
タイプ conference paper
著者 YAMAGUCHI, Takuma

× YAMAGUCHI, Takuma

WEKO 40121

YAMAGUCHI, Takuma

Search repository
MARUYAMA, Minoru

× MARUYAMA, Minoru

WEKO 40122

MARUYAMA, Minoru

Search repository
信州大学研究者総覧へのリンク
氏名 MARUYAMA, Minoru
URL http://soar-rd.shinshu-u.ac.jp/profile/ja.WCnCbpkh.html
出版者
出版者 International Conference on Pattern Recognition
引用
内容記述タイプ Other
内容記述 International Conference on Pattern Recognition: 1-4 2008
書誌情報
p. 1-4, 発行日 2008
抄録
内容記述タイプ Abstract
内容記述 With rapid increase of number of accessible images and videos, ability to recognize visual information is getting more and more important for content-based information retrieval. Recently, probabilistic topic models, which were originally developed for text analysis, have been used for image categorization successfully. Usually, topics which represent contents of an image is detected based on the underlying probabilistic model, then image categorization is carried out using topic distribution as the input feature. Typical method is to use k-nearest neighbor classifier based on L2-distance after topic discovery. In the method, topic distribution is just treated as a feature point. In this paper, we propose a categorization method based on more natural use of the topic distribution, which is derived by using pLSA model. Categorization is carried out by estimating conditional probability p(categoryjdata). We present two types of image categorization tasks, scene classification and document image segmentation, and show the proposed method performs very well. In addition, we also examine the performance of the proposed method under the situation where only the limited number of labeled examples are available. We show our method can perform quite well even in the circumstances.
資源タイプ(コンテンツの種類)
内容記述タイプ Other
内容記述 Article
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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