A Bayesian Local Binary Pattern texture descriptor

Abstract

In this paper, a Bayesian LBP operator is proposed. This operator is formulated in a novel Filtering, Labeling and Statistic (FLS) framework for texture descriptors. In the framework, the local labeling procedure, which is a part of many popular descriptors such as LBP, SIFT and VZ, can be modeled as a probability and optimization process. This enables the use of more reliable prior and likelihood information and reduces the sensitivity to noise. The BLBP operator pursues a label image, when given the filtered vector image, by maximizing the joint probability of two images under the criterion of MAP. The proposed approach is evaluated on texture retrieval schemes using entire Brodatz database. The result reveals BLBP operator's efficient performance and FLS framework's capability to in-depth analysis of the texture descriptors on a common background.