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Distributed multi-dimensional hidden Markov models for image and trajectory-based video classifications

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3 Author(s)
Xiang Ma ; Dept. of Electr. & Comput. Eng., Univ. of Illinois at Chicago, Chicago, IL ; Schonfeld, D. ; Khokhar, A.

In this paper, we propose a novel multi-dimensional distributed hidden Markov model (DHMM) framework. We first extend the theory of 2D hidden Markov models (HMMs) to arbitrary causal multi-dimensional HMMs and provide the classification and training algorithms for this model. The proposed extension of causal multi-dimensional HMMs allows state transitions in arbitrary causal directions and neighbors. We subsequently generalize this framework further to non-causal models by distributing the non-causal models into multiple causal multi-dimensional HMMs. The proposed training and classification process consists of the extension of three fundamental algorithms to multi-dimensional causal systems, i.e. (1) expectation-maximization (EM) algorithm; (2) general forward-backward (GFB) algorithm; and (3) Viterbi algorithm. Simulation results performed using real-world images and videos demonstrate the superior performance, higher accuracy rate and promising applicability of the proposed DHMM framework.

Published in:

Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on

Date of Conference:

March 31 2008-April 4 2008

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