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Cross-Modal Multivariate Pattern Analysis
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Texture analysis using generalized co-occurrence matrices.

L S Davis1, S A Johns, J K Aggarwal

  • 1Department of Computer Science, University of Texas at Austin, Austin, TX 78712.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel texture analysis method using generalized co-occurrence matrices (GCM) to capture spatial feature distribution in unsegmented textures. The approach enhances texture description and classification accuracy for natural textures.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Texture analysis is crucial for image understanding.
  • Existing methods often struggle with unsegmented textures.
  • A need exists for robust texture descriptors capturing spatial relationships.

Purpose of the Study:

  • To develop a new texture analysis approach based on spatial feature distribution.
  • To introduce Generalized Co-occurrence Matrices (GCM) for texture description.
  • To evaluate the proposed method on natural textures.

Main Methods:

  • Textures are represented by the spatial distribution of local features.
  • Generalized Co-occurrence Matrices (GCM) are defined using a spatial constraint predicate (F) and feature descriptions (di).
  • Features derived from GCMs are analyzed and experimentally validated.

Main Results:

  • The proposed GCM-based method effectively describes textures using spatial feature distributions.
  • Experimental results demonstrate the utility of the approach for natural texture analysis.
  • The method provides a new framework for quantitative texture analysis.

Conclusions:

  • The GCM approach offers a powerful new tool for texture analysis, especially for unsegmented textures.
  • This method advances the field by incorporating spatial relationships of local features.
  • Further research can explore diverse feature sets and spatial constraints within the GCM framework.