Latent topic model for image annotation by modeling topic correlation

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

3 引用 (Scopus)

抜粋

For the task of image annotation, traditional probabilistic topic models based on Latent Dirichlet Allocation (LDA) [1], assume that an image is a mixture of latent topics. An inevitable limitation of LDA is the inability to model topic correlation since topic proportions of an image are generated independently. Motivated by Correlated Topic Model (CTM) [2] which derives from natural language processing to model topic correlation of a document, we extend the popular LDA based models (corrLDA [3], sLDA-bin [4], trmmLDA [5]) to CTM based models (corrCTM, sCTM-bin, trmmCTM). We present a comprehensive comparison between CTM based and LDA based models on three benchmark datasets, illustrating the superior annotation performance of proposed CTM based models, by means of propagating topic correlation among image features and annotation words.

元の言語英語
ホスト出版物のタイトル2013 IEEE International Conference on Multimedia and Expo, ICME 2013
DOI
出版物ステータス出版済み - 10 21 2013
イベント2013 IEEE International Conference on Multimedia and Expo, ICME 2013 - San Jose, CA, 米国
継続期間: 7 15 20137 19 2013

その他

その他2013 IEEE International Conference on Multimedia and Expo, ICME 2013
米国
San Jose, CA
期間7/15/137/19/13

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

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  • これを引用

    Xu, X., Shimada, A., & Taniguchi, R-I. (2013). Latent topic model for image annotation by modeling topic correlation. : 2013 IEEE International Conference on Multimedia and Expo, ICME 2013 [6607531] https://doi.org/10.1109/ICME.2013.6607531