14 Statistical Features Of Gray Level Co-Remarkably Matrix Analysis

Haralick [68] defined 14 statistical features of gray-level co-occurrence matrix for texture classification such as entropy, energy, contrast, auto correlation and Level co-occurrence matrix method of representing texture feature has found useful application in recognizing fabric defects, and in rock texture classification and retrieval. The detailed description of Haralick Texture Feature and Tamura Texture Features given below:
3.1.2.1.1 Haralick Texture Feature Gray Level Co-occurrence Matrix (GLCM) a statistical method for examining texture features that consider the spatial relationship of pixels, also known as Gray Level Spatial Dependence. In this a GLCM matrix is created by calculating how often a pixel with the intensity value i
C=∑_(i,j)▒〖(i-j)^2 p(i,j)〗 (3.5) Correlation - Correlation measures how pixel is correlated to its neighbor over the whole image. C=∑_(i,j)▒(i-μ_i )(j-μ_j )p(i,j)/(δ_i δ_j ) (3.6) Entropy - Entropy gives measures of complexity of the image and this complex texture tends to higher entropy. E=∑_i▒∑_j▒〖P(i,j)〗 (3.7) Energy - Energy is the sum of squared elements in the GLCM and it is by default one for constant image. E=∑_(i,j)▒(i,j)^2 (3.8)

3.1.2.1.2 Tamura Texture Feature

According to quantitative analysis one of the first descriptions given by the Tamura [69] proposed six textural properties and gave descriptions common over all texture patterns in Broadtz’s photographic images. These are six different texture features given by Tamura Coarseness, Contrast, Directionality, Line-Likeness, Regularity and
Contrast - Contrast measures distribution of gray levels that varies in an image and to what extent its distribution is biased to black or white. The second order and normalized fourth–order central moments of the gray levels are used to define the contrast. Con=σ/(α_4 ) (3.11)
〖 α〗_4=μ_4/σ^4 (3.12)
Where, µ4 is the fourth moment about the mean and σ2 is the variance. n=1/4 to give the closest value according the Tamura. Directionality - It is measures by the frequency distribution of oriented local edge against their directional angles. It is a global property over a region. This texture feature given by Tamura does not differentiate between orientations or patterns but measures the total degree of directionality in an image is given by Dir. It is the most important feature given by Tamura about matrix to distinguish from another image that how much uniform the region is. Dir=1-rn_peaks ∑_(p=1)^(n_peaks)▒∑_(aϵw_p)▒〖(a-a_p )^2 H_dir (a) 〗