Improved Weber’s law based local binary pattern for dynamic texture recognition

Abstract

Dynamic texture is the moving sequence of images that shows some form of temporal regularity. Various static texture descriptors have been extended to spatiotemporal domain for dynamic texture classification. Local Binary Pattern (LBP) is a simple descriptor computationally but sensitive to noise and sometimes fails to capture different patterns. In view of this, a novel approach for dynamic texture classification is introduced that maintains the advantageous characteristics of uniform LBP. Inspired by the Weber's law, a simple yet very powerful, robust texture descriptor, i.e., Weber's law based LBP with center pixel (WLBPC) is proposed from the local patches based on the conventional Local Binary Pattern approach. A noise resistant variant of Weber's law based LBP with center pixel (NR-WLBPC) is also proposed. To do this, WLBPC is extended to a 3-valued code based on a new threshold. Proposed noise resistant variant of WLBPC descriptor makes use of the indecisive bit and the uniform pattern to compute the feature vector. Center pixel information is fused with both the dynamic texture descriptors to improve the discriminative power. Extensive experimental evaluations on representative dynamic texture databases (DynTex++ and UCLA) show that the proposed descriptors show better performance in comparison to recent state-of-the-art LBP variants and other methods under both normal and noisy conditions. Noise invariant of the proposed descriptor also performs better in the presence of noise due to its robustness and discriminating capabilities.