Texture Analysis Using Generalized Co-Occurrence Matrices

Authors:
Larry S. Davis;Steven A. Johns;J. K. Aggarwal
Affiliations:
Department of Computer Science, University of Texas at Austin, Austin, TX 78712.;Abbott Laboratories, Dallas, TX.;FELLOW, IEEE, Department of Electrical Engineering, University of Texas at Austin, Austin, TX 78712.
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1979

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Abstract

We present a new approach to texture analysis based on the spatial distribution of local features in unsegmented textures. The textures are described using features derived from generalized co-occurrence matrices (GCM). A GCM is determined by a spatial constraint predicate F and a set of local features P = {(Xi, Yi, di), i = 1,..., m} where (Xi, Yi) is the location of the ith feature, and di is a description of the ith feature. The GCM of P under F, GF, is defined by GF(i, j) = number of pairs, pk, pl such that F(pk, pl) is true and di and dj are the descriptions of pk and pl, respectively. We discuss features derived from GCM's and present an experimental study using natural textures.