Hi,

in principle you can categorize features as follow:

• Structural-Based

The structural-based texture analysis approach is the most intuitive one, because

it is in the nature of the human being to notice the structural texture characteristics and the arrangement of the texture primitives, respectively.

e.g., shape descriptors like area, perimeter, orientation, centroid, eccentricity, elongation, compactness, Euler number, intensity, magnitude, moments, etc.,

• Statistical-Based

Describes the relationship to their neighborhoods can be calculated by using first, second, and higher order statistics. e.g. Haralick

first order statistics: e.g. features from gray level histogram like average intensities

second order statistics: properties for a pair of gray-level pixels and are used to, e.g., compute the length, or orientation,of a line with the endpoints.

higher order statistics: calculate the statistical properties for 3 and more gray-level pixels and cover the neighbourly relations, e.g., compute the skewness or kurtosis of a gray-level distribution. But , higher order statistics are less robust against noise

• Model-Based

The model-based texture analysis domain mainly addresses the construction of a stochastic or fractal model which represents the texture in an image and with the aim of using the estimated parameters of the model for texture analysis. However, these approaches are suitable for micro-textures but not for macro-textures. E.g. Markov Random Fields, Autoregressive Models or Fractal Models.

• Transform-Based

Aside from the spatial information, the spectral analysis of an image opens new possibilities for analysis and delivers further information. The most important transformations are fast Fourier transformation (FFT) and Wavelet transformation. Compared to the fast Fourier transformation, with wavelets it is possible to characterize the local shape of a texture and not only sharp variations of it. Furthermore, the advantages compared to commonly used hierarchical bases are manifold. In principle they are orthonormal, linear, continuous, and continuous invertible.

Hope this helps!

Best

Hi, Thanks for the good question an nice and clear  answer it is helped me lot for my research work. thank you both of you..