In this paper, we propose a new context-sensitive Bayesian learning algorithm. By modeling the distributions of data locations by a mixture of Gaussians, the new algorithm can uti...
A novel illumination invariant unsupervised multispectral texture segmentation method with unknown number of classes is presented. Multispectral texture mosaics are locally repres...
Moment matching is a popular means of parametric density estimation. We extend this technique to nonparametric estimation of mixture models. Our approach works by embedding distri...
Locally adaptive classifiers are usually superior to the use of a single global classifier. However, there are two major problems in designing locally adaptive classifiers. First,...
Juan Dai, Shuicheng Yan, Xiaoou Tang, James T. Kwo...
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...