A compact multi-pattern encoding descriptor for texture classification

Abstract Binary pattern family is considered as a powerful tool for visual texture classification. Most popular methods improve the classification performance by multi-feature fusion. However, many sub-features are redundant and low-discriminative and the classification system has high computational complexity and unsatisfactory results. To handle above problems, this paper proposes a compact multi-pattern encoding descriptor for visual texture classification. First, we develop local extremum patterns and local center pattern to represent the neighborhood intensity changes. Then, we design a compact encoding scheme to encode local maximum, minimum and center patterns into a three-bit binary code, named MMC pattern. Finally, a compact multi-pattern encoding descriptor is proposed by combining the traditional local sign pattern and MMC pattern. Experimental results on five representative texture databases demonstrate that our method achieves the state-of-the-art texture classification performance.