Addressing the problems of Bayesian network classification of video using high-dimensional features

Bayesian theory is of great interest in pattern classification. We present an approach to aid in the effective application of Bayesian networks in tasks like video classification, where descriptors originate from varied sources and are large in number. In order to extend the application of conventional Bayesian theory to the case of continuous and nonparametric descriptor space, dimension partitioning into attributes by minimizing the discrete Bayes error is proposed. The partitioning output goes to the dimensionality reduction module. A new algorithm for dimensionality reduction for improving the classification accuracy is proposed based on the class pair discriminative capacity of the dimensions. It is also shown how attributes can be weighed automatically in a single-label assignment based on comparing the class pairs. A computationally efficient method to assign multiple labels on the samples is also presented. Comparison with standard classification tools on video data of more than 4000 segments shows the potential of our approach in pattern classification.