Uniform and Rotation Invariant Texture Model for Material Recognition
Local binary pattern (LBP) and its variants have shown promising results in visual recognition applications.
However, most existing approaches rely on a pre-defined structure to extract LBP features. We argue that the optimal LBP
structure should be task-dependent and propose a new method to learn discriminative LBP structures. We formulate it as a
point selection problem: Given a set of point candidates, the goal is to select an optimal subset to compose the LBP structure.
In view of the problems of current feature selection algorithms, we propose a novel Maximal Joint Mutual Information
criterion. Then, the point selection is converted into a binary quadratic programming problem and solved efficiently via the
branch and bound algorithm. The proposed LBP structures demonstrate superior performance to the state-of-the-art
approaches on classifying both spatial patterns in scene recognition and spatial-temporal patterns in dynamic texture
Keywords- LBP structure optimization, maximal joint mutual information, binary quadratic programming, scene
recognition, dynamic texture recognition