Texture Classification Based on Complex Network Model with Spatial Information

• Thewsuwan, Srisupang

• Horio, Keiichi

抄録

This paper proposes a method for image texture classification based on a complex network model. Finding relevant and valuable information in an image texture is an essential issue for image classification and remains a challenge. Recently, a complex network model has been used for texture analysis and classification. However, with current analysis methods, important empirical properties of image texture such as spatial information are discarded from consideration. Accordingly, we propose local spatial pattern mapping (LSPM) method for manipulating the spatial information in an image texture with multi-radial distance analysis to capture the texture pattern. In experiments, the feature properties under the traditional complex network model and those with the proposed method are analyzed by using the Brodatz, UIUC, and Outex databases. As results, the proposed method is shown to be effective for texture classification, providing an improved classification rate as compared to the traditional complex network model.

収録刊行物

• International Journal of Innovative Computing, Information and Control

  International Journal of Innovative Computing, Information and Control 14 (3), 1113-1121, 2018-06

  ICIC International

キーワード

• Complex network
• Spatial arrangement
• Spatial information
• Texture analysis
• Texture classification

詳細情報

• CRID: 1050001338911969920
• NII論文ID: 120006471556
• ISSN: 13494198
• HANDLE: 10228/00006770
• Web Site: https://kyutech.repo.nii.ac.jp/records/5559
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
  • IRDB
  • CiNii Articles