LBP-Structure Optimization With Symmetry and Uniformity Regularizations for Scene Classification

Abstract

Local binary pattern (LBP) and its variants have been widely used in many visual recognition tasks. Most existing approaches utilize predefined LBP structures to extract LBP features. Recently, data-driven LBP structures have shown promising results. However, due to the limited number of training samples, data-driven structures may overfit the training samples, hence could not generalize well on the novel testing samples. To address this problem, we propose two structural regularization constraints for LBP-structure optimization: symmetry constraint and uniformity constraint. These two constraints are inspired by predefined LBP structures, which convey the human prior knowledge on designing LBP structures. The LBP-structure optimization is casted as a binary quadratic programming problem and solved efficiently via the branch-and-bound algorithm. The evaluation on two scene-classification datasets demonstrates the superior performance of the proposed approach compared with both predefined LBP structures and unconstrained data-driven LBP structures.