Local spiking pattern and its application to rotation- and illumination-invariant texture classification

Abstract

Automatic classification of texture images is an important and challenging task in the applications of image analysis and scene understanding. In this paper, we focus on the problem of the classification of texture images acquired under various rotation and illumination conditions and propose a new local image descriptor which is named local spiking pattern (LSP). Specifically, the proposed LSP uses a 2-dimensional neural network, which is made up of a series of interconnected spiking neurons, to generate binary images by iteration. The binary images are then encoded to generate discriminative feature vectors. In classification phase, we use a nearest neighborhood classifier to achieve supervised classification. Finally, LSP is evaluated by comparison with some state-of-the-art local image descriptors. Experimental results on Outex texture database show that LSP outperforms most of the other local image descriptors in the noiseless case and shows high robustness when texture images are distorted by salt & pepper noise.