A multi-scale texture segmentation method

Jiannong Cao, Yu Wei Dong, Ping Lu Wang, Qi Gao Xu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

In order to realize texture image segmentation, a new texture segmentation method is presented. The method fused with Local Binary Pattern (LBP), Gray-level Co-occurrence Matrix (GLCM) and permutation entropy. After processing the texture image by the rotation invariant LBP operator, the LBP image can be obtained. Then co-occurrence matrixes can be calculated in the LBP image, some features which can describe texture can be obtained in these matrixes, and in the original image, some permutation entropies can be calculated in many different directions. After combining those features and permutation entropies, a feature vector can be built. According to this feature vector, the method made the texture segmentation in multiscale. The center of a homogenous texture is analyzed by using features in coarse resolution and its border is detected in finer resolution so as to locate the boundary accurately. Compared with the method based on permutation entropy and gray feature, the presented method shows visible improvements both in segmentation accuracy, and in increasing boundary precision and region harmony.
Original languageEnglish
Title of host publication2014 10th International Conference on Natural Computation, ICNC 2014
PublisherIEEE
Pages873-877
Number of pages5
ISBN (Electronic)9781479951505
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event2014 10th International Conference on Natural Computation, ICNC 2014 - Xiamen, China
Duration: 19 Aug 201421 Aug 2014

Conference

Conference2014 10th International Conference on Natural Computation, ICNC 2014
Country/TerritoryChina
CityXiamen
Period19/08/1421/08/14

Keywords

  • Co-occurrence matrix
  • Local binary pattern (LBP)
  • Multi-scale segmentation
  • Permutation entropy
  • Texture segmentation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering

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