• an improved hierarchical dirichlet process-hidden markov model and its application to trajectory modeling and retrieval

    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 856
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     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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