Latent topic model for image annotation by modeling topic correlation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Multimedia and Expo, ICME 2013
DOIs
Publication statusPublished - Oct 21 2013
Event2013 IEEE International Conference on Multimedia and Expo, ICME 2013 - San Jose, CA, United States
Duration: Jul 15 2013Jul 19 2013

Other

Other2013 IEEE International Conference on Multimedia and Expo, ICME 2013
CountryUnited States
CitySan Jose, CA
Period7/15/137/19/13

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

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  • Cite this

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