Local Directional Difference and Relational Descriptor for Texture Classification

Weidan Yan and Yongsheng Dong ()
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Weidan Yan: School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China
Yongsheng Dong: School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China

Mathematics, 2024, vol. 12, issue 21, 1-15

Abstract: The local binary pattern (LBP) has been widely used for extracting texture features. However, the LBP and most of its variants tend to focus on pixel units within small neighborhoods, neglecting differences in direction and relationships among different directions. To alleviate this issue, in this paper, we propose a novel local directional difference and relational descriptor (LDDRD) for texture classification. Our proposed LDDRD utilizes information from multiple pixels along the radial direction. Specifically, a directional difference pattern (DDP) is first extracted by performing binary encoding on the differences between the central pixel and multiple neighboring pixels along the radial direction. Furthermore, by taking the central pixel as a reference, we extract the directional relation pattern (DRP) by comparing binary encodings representing different directions. Finally, we fuse the above DDP and DRP to form the LDDRD feature vector. Experimental results on six texture datasets reveal that our proposed LDDRD is effective and outperforms eight representative methods.

Keywords: texture classification; local binary pattern; local feature; multi-direction; noise (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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