Abstract
In this paper, a new method has been proposed to calculate the differential excitation and orientation components of a texture descriptor, called the Weber local descriptor (WLD), so that not only is the computational complexity of this method reduced, but the orientation component also becomes rotation invariant. Then to classify the color texture images in RGB color space, the proposed modified WLD will be used so that, by extracting WLD features from R, G, and B components of the color space, a one-dimensional histogram is obtained that describes the image. The results of the classification tests performed on four well-known data sets show the effectiveness of the proposed method.
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