DocumentCode :
1116748
Title :
An Empirical Evaluation of Generalized Cooccurrence Matrices
Author :
Davis, L.S. ; Clearman, M. ; Aggarwal, J.K.
Author_Institution :
Department of Computer Sciences, University of Texas at Austin, Austin, TX 78712.
Issue :
2
fYear :
1981
fDate :
3/1/1981 12:00:00 AM
Firstpage :
214
Lastpage :
221
Abstract :
A comparative study of generalized cooccurrence texture analysis tools is presented. A generalized cooccurrence matrix (GCM) reflects the shape, size, and spatial arrangement of texture features. The particular texture features considered in this paper are 1) pixel-intensity, for which generalized cooccurrence reduces to traditional cooccurrence; 2) edge-pixel; and 3) extended-edges. Three experiments are discussed-the first based on a nearest neighbor classifier, the second on a linear discriminant classifier, and the third on the Battacharyya distance figure of merit.
Keywords :
Associative memory; Bayesian methods; Gratings; Image edge detection; Machine learning; Pattern recognition; Shape; Surface texture; Surface topography; Vectors; Edge detection; image texture analysis; pattern recognition;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.1981.4767084
Filename :
4767084
Link To Document :
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