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:
- Contrast
- Dissimilarity (DIS)
- Homogeneity (HOM)
Orderliness Group Features:
- Angular Second Moment (ASM)
- Maximum Probability (MAX)
- Entropy (ENT)
Statistics Group Features:
- GLCM Mean
- GLCM Variance
- GLCM Correlation
[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.