Texture Classification by using Advanced Local Binary Patterns and Spatial Distribution of Dominant Patterns

In this paper, we propose a new feature extraction method, which is robust against rotation and histogram equalization for texture classification. To this end, we introduce the concept of Advanced Local Binary Patterns (ALBP), which reflects the local dominant structural characteristics of different kinds of textures. In addition, to extract the global spatial distribution feature of the ALBP patterns, we incooperate ALBP with the Aura Matrix measure as the second layer to analyze texture images. The proposed method has three novel contributions. (a) The proposed ALBP approach captures the most essential local structure characteristics of texture images (i.e. edges, corners); (b) the proposed method extracts global information by using Aura Matrix measure based on the spatial distribution information of the dominant patterns produced by ALBP; and (c) the proposed method is robust to rotation and histogram equalization. The proposed approach has been compared with other widely used tex...

• Type: Conference
• Year: 2007
• Where: ICASSP
• Authors: Shu Liao, Albert C. S. Chung
• Keywords: Aura Matrix Measure, Histogram Equalization, ICASSP 2007, Signal Processing, Texture Classification