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    Non-parametric hidden conditional random fields for action classification

    Maybank, Stephen J. and Raman, Natraj (2016) Non-parametric hidden conditional random fields for action classification. In: UNSPECIFIED (ed.) 2016 International Joint Conference on Neural Networks (IJCNN) - Proceedings. New Jersey, U.S.: IEEE Computer Society, pp. 3256-3263. ISBN 9781509006205.

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    Conditional Random Fields (CRF), a structured prediction method, combines probabilistic graphical models and discriminative classification techniques in order to predict class labels in sequence recognition problems. Its extension the Hidden Conditional Random Fields (HCRF) uses hidden state variables in order to capture intermediate structures. The number of hidden states in an HCRF must be specified a priori. This number is often not known in advance. A non-parametric extension to the HCRF, with the number of hidden states automatically inferred from data, is proposed here. This is a significant advantage over the classical HCRF since it avoids ad hoc model selection procedures. Further, the training and inference procedure is fully Bayesian eliminating the over fitting problem associated with frequentist methods. In particular, our construction is based on scale mixtures of Gaussians as priors over the HCRF parameters and makes use of Hierarchical Dirichlet Process (HDP) and Laplace distribution. The proposed inference procedure uses elliptical slice sampling, a Markov Chain Monte Carlo (MCMC) method, in order to sample optimal and sparse posterior HCRF parameters. The above technique is applied for classifying human actions that occur in depth image sequences – a challenging computer vision problem. Experiments with real world video datasets confirm the efficacy of our classification approach.


    Item Type: Book Section
    Additional Information: IEEE World Congress on Computational Intelligence, 24-29 Jul 2016, Vancouver, Canada. ISSN: 2161-4407. (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Keyword(s) / Subject(s): action classification, depth video, HCRF, HDP, Laplace distribution, elliptical slice sampling
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Administrator
    Date Deposited: 16 Nov 2016 15:09
    Last Modified: 09 Aug 2023 12:38


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