An ICA Mixture Hidden Markov Model for Video Content Analysis

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An ICA Mixture Hidden Markov Model for Video Content Analysis
In this paper, a new theoretical framework based on hidden Markov model (HMM) and independent component analysis (ICA) mixture model is presented for content analysis of video, namely ICAMHMM. Unlike the Gaussian mixture observation model commonly used in conventional HMM applications, the observations in the new ICAMHMM are modeled as a mixture of non-Gaussian components. Each non-Gaussian component is formulated by an ICA mixture, reflecting the independence of different components across video frames. In addition, to construct a compact feature space to represent a video frame, ICA is applied on video frames and the ICA coefficients are used to form a compact 2-D feature subspace that makes the subsequent modeling computationally efficient. The model parameters can be identified using supervised learning by the training sequences. The new re-estimation learning formulae of iterative ICAMHMM parameter estimation are derived based on a maximum likelihood function. Employing the identi...
Jian Zhou, Xiao-Ping Zhang
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TCSV
Authors Jian Zhou, Xiao-Ping Zhang
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