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An Improved Hierarchical Dirichlet Process-Hidden Markov Model and Its Application to Trajectory Modeling and Retrieval

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Abstract

In this paper, we propose a hierarchical Bayesian model, an improved hierarchical Dirichlet process-hidden Markov model (iHDP-HMM), for visual document analysis. The iHDP-HMM is capable of clustering visual documents and capturing the temporal correlations between the visual words within a visual document while identifying the number of document clusters and the number of visual topics adaptively. A Bayesian inference mechanism for the iHDP-HMM is developed to carry out likelihood evaluation, topic estimation, and cluster membership prediction. We apply the iHDP-HMM to simultaneously cluster motion trajectories and discover latent topics for trajectory words, based on the proposed method for constructing the trajectory word codebook. Then, an iHDP-HMM-based probabilistic trajectory retrieval framework is developed. The experimental results verify the clustering accuracy of the iHDP-HMM and trajectory retrieval accuracy of the proposed framework.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Weiming Hu, Guodong Tian & Xi Li

  2. Department of Computer Science and Information Systems, Birkbeck College, Malet Street, London, WC1E 7HX, UK

    Stephen Maybank

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  1. Weiming Hu
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  2. Guodong Tian
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Correspondence to Xi Li.

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Hu, W., Tian, G., Li, X. et al. An Improved Hierarchical Dirichlet Process-Hidden Markov Model and Its Application to Trajectory Modeling and Retrieval. Int J Comput Vis 105, 246–268 (2013). https://doi.org/10.1007/s11263-013-0638-8

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  • DOI: https://doi.org/10.1007/s11263-013-0638-8

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