A Completed Multiple Threshold Encoding Pattern for Texture Classification

Authors

  • Bin Li Harbin Engineering University
  • Yibing Li Harbin Engineering University
  • Q. M. Jonathan Wu Univeristy of Windsor

DOI:

https://doi.org/10.5566/ias.2824

Keywords:

binary pattern, completed encoding, image texture analysis, texture image classification

Abstract

The binary pattern family has drawn wide attention for texture representation due to its promising performance and simple operation. However, most binary pattern methods focus on local neighborhoods but ignore center pixels. Even if some studies introduce the center based sub-pattern to provide complementary information, existing center based sub-patterns are much weaker than other local neighborhood based sub-patterns. This severe unbalance limits the classification performance of fusion features significantly. To alleviate this problem, this paper designs a multiple threshold center pattern (MTCP) to provide a more discriminative and complementary local texture representation with a compact form. First, a multiple threshold encoding strategy is designed to encode the center pixel that generates three 1-bit binary patterns. Second, it adopts a compact multi-pattern encoding strategy to combine them into a 3-bit MTCP. Furthermore, this paper proposes a completed multiple threshold encoding pattern by fusing the MTCP, local sign pattern, and local magnitude pattern. Comprehensive experimental evaluations on three popular texture classification benchmarks confirm that the completed multiple threshold encoding pattern achieves superior texture classification performance.

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Published

2023-10-22

How to Cite

Li, B., Li, Y., & Wu, Q. M. J. (2023). A Completed Multiple Threshold Encoding Pattern for Texture Classification. Image Analysis and Stereology, 42(3), 145–159. https://doi.org/10.5566/ias.2824

Issue

Vol. 42 No. 3 (2023)

Section

Original Research Paper

License

Copyright (c) 2023 Bin Li, Yibing Li, Q. M. Jonathan Wu

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.