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

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 noncausal 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 realworld images and videos demonstrate the superior performance, higher accuracy rate and promising applicability of the proposed DHMM f...

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Xiang Ma, Dan Schonfeld, Ashfaq A. Khokhar
• Keywords: Arbitrary Causal, Causal Multi-dimensional Hmms, Hidden Markov Models, ICASSP 2008, Signal Processing