Grey Level Co-occurrence Matrix

The spatial relationship between neighboring or adjacent pixels is provided by GLCM. We will gauge some texture features from this GLCM matrix. The second-order statistical texture analysis method is called GLCM. It measures the frequency with which pixels in a given direction and distance d are present in an image and examines the spatial relationship between them. A gray-level spatial dependence matrix is another name for a gray-level co-occurrence matrix. By figuring out how frequently two pixels with the same grayscale intensity value—i and j—occur horizontally next to one another, the gray matrix generates the GLCM.

Syntax:

glcms = graycomatrix(I)

glcms = graycomatrix(I,Name,Value)

[glcms,SI] = graycomatrix(___)

• glcms- From image I, gray matrix(I) generates a gray-level co-occurrence matrix (GLCM), which is also known as a gray-level spatial dependence matrix.
• glcms- Depending on the values of the optional name-value pair arguments, the gray matrix(I, Name, Value) returns one or more gray-level co-occurrence matrices.
• [glcms,SI]- The scaled image, SI, used to calculate the gray-level co-occurrence matrix is returned by gray matrix(___).

The use of texture to identify regions of interest in an image is a crucial characteristic. One of Haralick et al.’s earliest approaches to texture feature extraction was Grey Level Co-occurrence Matrices (GLCM)  in the year 1973. Since then, it has been used extensively in a number of texture analysis applications and continues to be a significant technique for feature extraction in texture analysis. Haralick used the GLCMs to extract fourteen features to describe texture. In CBIR applications, Dacheng et al utilized 3D co-occurrence matrices. For the purpose of matching and recognizing objects, Kovalev and Petrov made use of special multidimensional co-occurrence matrices. Clustering methods make use of multi-dimensional texture analysis, which was first demonstrated. Co-occurrence matrices and their extraction of additional features from n-dimensional Euclidean spaces are the aims of this work, which extends the concept to these spaces. In CBIR applications, the newly defined features are found to be useful.