Multi-modal hierarchical dirichlet process model for predicting image annotation and image-object label correspondence

Oksana Yakhnenko, Vasant Honavar

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

14 Scopus citations

Abstract

Many real-world applications call for learning predictive relationships from multi-modal data. In particular, in multi-media and web applications, given a dataset of images and their associated captions, one might want to construct a predictive model that not only predicts a caption for the image but also labels the individual objects in the image. We address this problem using a multi-modal hierarchical Dirichlet Process model (MoM-HDP) - a stochastic process for modeling multi-modal data. MoM-HDP is an analog of a multi-modal Latent Dirichlet Allocation (MoM-LDA) with an infinite number of mixture components. Thus MoM-HDP allows circumventing the need for a priori choice of the number of mixture components or the computational expense of model selection. During training, the model has access to an un-segmented image and its caption, but not the labels for each object in the image. The trained model is used to predict the label for each region of interest in a segmented image. The model parameters are estimated efficiently using variational inference. We use two large benchmark datasets to compare the performance of the proposed MoM-HDP model with that of MoM-LDA model as well as some simple alternatives: Naive Bayes and Logistic Regression classifiers based on the formulation of the image annotation and image-label correspondence problems as one-against-all classification. Our experimental results show that unlike MoM-LDA, the performance of MoM-HDP is invariant to the number of mixture components. Furthermore, our experimental evaluation shows that the generalization performance of MoM-HDP is superior to that of MoM-HDP as well as the one-against-all Naive Bayes and Logistic Regression classifiers.

Original languageEnglish (US)
Title of host publicationSociety for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133
Pages280-290
Number of pages11
StatePublished - 2009
Event9th SIAM International Conference on Data Mining 2009, SDM 2009 - Sparks, NV, United States
Duration: Apr 30 2009May 2 2009

Publication series

NameSociety for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics
Volume1

Other

Other9th SIAM International Conference on Data Mining 2009, SDM 2009
Country/TerritoryUnited States
CitySparks, NV
Period4/30/095/2/09

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Software
  • Applied Mathematics

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