Gray-level co-occurrence matrices as features in edge enhanced images

In 1973, Haralick, Shanmugam, and Dinstein published a paper in the IEEE Transactions on Systems, Man, and Cybernetics which proposed using Gray-Level Cooccurrence Matrices (GLCM) as a basis to define 2-D texture1. Over 14 different texture measures were defined using GLCM. In images with n × n grey levels, the size of the GLCM would be n × n which, for large n such as n=256, put a large computational load on the process and was also best suited for pixel distributions that were rather stochastic in nature. Such features as entropy, variance, correlation, etc. were proposed using the GLCM. When attempting to provide feature measures for man-made targets, most of the information contained in the target is contained by its edge distribution. Previous approaches form an edge outline of the target and then use some techniques such as Fourier descriptors to represent the target. However, in this case, extra steps need to be taken in order to assure that the edge outline is continuous or gaps in the outline somehow are dealt with when creating the Fourier coefficients for the feature vector. This paper presents an approach using GLCM where the gray scale image is put through an edge enhancement using any one of several edge operators. The resultant image is a binary image. For each point in the edge image, a 2×2 GLCM is created by placing an n × n window centered around the point and using the n2 neighboring points to build the GLCM's. This window should be sufficiently large to enclose the target of interest and the GLCM created provides the elements needed to define the features for the edge enhanced target. All software was created in Matlab2 using Matlab functions.