Orientation selectivity based structure for texture classification

• Main Authors: Wu, Jinjian, Lin, Weisi, Shi, Guangming, Zhang, Yazhong, Lu, Liu
• Other Authors: Dai, Qionghai
• Format: Conference or Workshop Item
• Language: English
• Published: 2015
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
• Online Access: https://hdl.handle.net/10356/106712; http://hdl.handle.net/10220/25123
• Institution: Nanyang Technological University

Local structure, e.g., local binary pattern (LBP), is widely used in texture classification. However, LBP is too sensitive to disturbance. In this paper, we introduce a novel structure for texture classification. Researches on cognitive neuroscience indicate that the primary visual cortex presents remarkable orientation selectivity for visual information extraction. Inspired by this, we investigate the orientation similarities among neighbor pixels, and propose an orientation selectivity based pattern for local structure description. Experimental results on texture classification demonstrate that the proposed structure descriptor is quite robust to disturbance.