2. ABOUT GLCM
Feature extraction can be done using many different techniques, of which Gray level co-occurrence matrix (GLCM) is the widely used method. GLCM has the second order statistical data of neighboring pixels of an image. The details of the image content can be obtained from GLCM through textural properties. It also shares the information regarding the association of the neighboring pixels to each other. The difference in patterns of image pixel values in an image is given accurately by GLCM. Patterns are certain kind of statistics that are used on each GLCM.
(i) Gray level occurrence matrix
These matrices are one which gives information about the presence of a texture in an image. The information about their presence that is uniform or irregular presence of the texture is well explained in this matrix. These matrices are invariant to rotation.
(ii) Haralick Statistics
The design of Haralick texture classification is that this statistics gives information regarding the general design of the pattern of neighboring pixels in a given image.
(iii) Gabour Features
The Gabor wavelets are built using the Gabor filter method. In this method, a band pass filter is applied to the input at different orientations and scales, similar to the mechanism used by the human visual system.
The basic steps involved in this process are,
1. Quantization
2. Creation of GLCM
3. Calculation of feature