Texture Analysis

Grey Level Co-occurance Matrix

Spatial information in the form of texture features can be useful for image classification. Texture measures can produce new images by making use of spatial information inherent in the image. Texture is the pattern of intensity variations in an image and can be a valuable tool in improving land-cover classification accuracy. Texture information involves the information from neighbouring pixels which is important to characterize the identified objects or regions of interest in an image.
The Gray Level Co-occurrence Matrix (GLCM) proposed by Haralik[R-1] is one of the most widely used methods to compute second order texture measures. Several texture features can be computed from the GLCM matrix, e.g., angular second moment, contrast, correlation, entropy, variance, inverse difference moment, difference average, difference variance, difference entropy, sum average, sum variance and sum entropy (Haralick[R-1]). Each feature models different properties of the statistical relation of pixels co-occurrence estimated within a given moving window and along predefined directions and inter-pixel distances.
The GLCM is a measure of the probability of occurrence of two grey levels separated by a given distance in a given direction. The features can be categorized into three groups, i.e. contrast group, orderliness group and statistics group.

Contrast Group Features:
 
Orderliness Group Features:
 
Statistics Group Features:
 
[R-1]    Haralick, R.M., Shanmugam, K., Denstien, I., “Textural features for image classification,” IEEE Trans Syst Man Cybern, vol. 3, no. 6, pp.610–621, 1973.