The aim of this paper is to present a study where we evaluate the optimal inclusion of the texture orientation
in the classification process. In this paper the orientation for each pixel in the image is extracted using the
partial derivatives of the Gaussian function and the main focus of our work is centred on the evaluation of
the local dominant orientation (which is calculated by combining the magnitude and local orientation) on
the classification results. While the dominant orientation of the texture depends strongly on the observation
scale, in this paper we propose to evaluate the macro-texture by calculating the distribution of the dominant
orientations for all pixels in the image that sample the texture at micro-level. The experimental results were
conducted on standard texture databases and the results indicate that the dominant orientation calculated at
micro-level is an appropriate measure for texture description