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Updated: May 29, 2026
13:51
Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
An empirical evaluation of generalized cooccurrence matrices.
L S Davis1, M Clearman, J K Aggarwal
1Department of Computer Sciences, University of Texas at Austin, Austin, TX 78712.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
This study compares generalized cooccurrence matrix (GCM) tools for texture analysis. GCM effectively captures texture features like pixel intensity, edges, and extended edges using various classifiers.
Area of Science:
- Image analysis
- Computer vision
- Pattern recognition
Background:
- Texture analysis is crucial for image interpretation.
- Generalized Cooccurrence Matrix (GCM) offers a flexible framework for texture characterization.
- Traditional cooccurrence methods are limited in capturing complex texture properties.
Purpose of the Study:
- To comparatively evaluate generalized cooccurrence matrix (GCM) based texture analysis tools.
- To assess the efficacy of GCM in analyzing diverse texture features.
- To investigate the performance of GCM across different classification algorithms.
Main Methods:
- Implementation of Generalized Cooccurrence Matrix (GCM) for texture feature extraction.
- Analysis of three texture feature types: pixel-intensity, edge-pixel, and extended-edges.
- Evaluation using three experimental setups: nearest neighbor classifier, linear discriminant classifier, and Bhattacharyya distance.
Main Results:
- GCM successfully represents shape, size, and spatial arrangement of texture features.
- Comparative performance analysis across different classifiers and feature types.
- Demonstration of GCM's versatility in texture analysis tasks.
Conclusions:
- Generalized cooccurrence matrix (GCM) provides a robust method for texture analysis.
- The GCM framework is adaptable to various texture features and classification approaches.
- This study highlights the potential of GCM in advancing image analysis and pattern recognition.
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