Asymmetric Gaussian-Based Statistical Models Using Markov Chain Monte Carlo Techniques for Image Categorization

Abstract

A novel unsupervised Bayesian image categorization framework based on asymmetric Gaussian mixture (AGM) model is proposed and the mixture parameter estimation is achieved by sampling-based reversible jump Markov chain Monte Carlo (RJMCMC) method. Previous researches have reveled that AGM outperforms classic symmetric mixture models (i.e Gaussian mixture model (GMM)) since the model adapts both symmetric and asymmetric datasets yielding better fitting accuracy. Moreover, the introduction of RJMCMC, a hybrid self-adapted sampling-based MCMC implementation, enables model transfer throughout parameter learning process, therefore, automatically converges to the optimal number of categories. In order to better identify visual features from the challenging UIUC sport events dataset, the image representative data is generated by adopting scale-invariant feature transform (SIFT), bag-of-visual-words (BOVW) and probabilistic latent semantic analysis (pLSA) techniques. Eventually, irrelevant and unneeded information will be filtered by feature selection. A comparison between AGM and other popular classifiers is given to discover its merits and the direction of future work is suggested.