Texture Feature Extraction using Gray-Level Co-occurrence Matrix

The Gray-Level Co-occurrence Matrix (GLCM) is a fundamental tool in image processing for texture feature extraction. It characterizes image texture by statistically analyzing the frequency of pixel pairs with specific gray-level values occurring at defined distances and orientations. Based on GLCM, numerous feature parameters can be computed, with entropy and energy being two commonly used texture descriptors. In code implementation, GLCM calculation typically involves iterating through image pixels while counting co-occurring pixel pairs based on specified offsets (distance and angle parameters).

Entropy measures the complexity and randomness of image texture. When elements in the GLCM are uniformly distributed, entropy values are higher, indicating complex texture patterns; conversely, lower entropy values suggest more homogeneous textures. Entropy calculation relies on the probability distribution of elements within the GLCM, effectively quantifying the image's information content. The mathematical implementation involves summing -p(i,j)*log(p(i,j)) across all matrix elements, where p(i,j) represents the normalized probability value at position (i,j) in the GLCM.

Energy, also known as angular second moment, evaluates the uniformity of gray-level distribution and texture coarseness. When GLCM elements concentrate around certain values, energy is high, indicating regular textures with minimal variation; lower energy values suggest more irregular or detailed textures. The computational formula for energy is the sum of squared GLCM elements (Σp(i,j)²), which can be efficiently implemented using element-wise multiplication and summation operations in programming languages like MATLAB or Python with NumPy.

In practical applications (such as remote sensing image analysis, medical imaging processing, etc.), GLCM combined with parameters like entropy and energy effectively distinguishes different texture patterns, finding widespread use in image classification and object recognition. Appropriate selection of pixel pair distance and orientation parameters can enhance feature sensitivity to specific textures. Programmatically, this involves testing different offset parameters (e.g., [0 1] for horizontal neighbors, [1 0] for vertical) to optimize feature discrimination for particular texture types.