Correlated topic model for image annotation

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

1 Citation (Scopus)

Abstract

For the task of image annotation, traditional methods based on probabilistic topic model, such as correspondence Latent Dirichlet Allocation (corrLDA) [1], assumes that image is a mixture of latent topics. However, this kind of models is unable to directly model correlation between topics since topic proportions of an image are generated independently. Our model, called correspondence Correlated Topic Model (corrCTM), extends Correlated Topic Model (CTM) [2] from natural language processing to capture topic correlation from covariance structure of more flexible model distribution. Unlike previous LDA based models, topic proportions are correlated with each other in proposed corrCTM. And the topic correlation propagates from image features to annotation words through a generative process, and finally correspondence between images and words could be generated. We derive an approximate inference and estimation algorithm based on variational method. We examine the performance of our model on two benchmark image datasets, show improved performance over corrLDA for both annotation and modeling word correlation.

Original languageEnglish
Title of host publicationFCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision
Pages201-208
Number of pages8
DOIs
Publication statusPublished - Apr 15 2013
Event19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2013 - Incheon, Korea, Republic of
Duration: Jan 30 2013Feb 1 2013

Other

Other19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2013
CountryKorea, Republic of
CityIncheon
Period1/30/132/1/13

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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    Xu, X., Shimada, A., & Taniguchi, R-I. (2013). Correlated topic model for image annotation. In FCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision (pp. 201-208). [6485488] https://doi.org/10.1109/FCV.2013.6485488