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In the automatic classification of colored natural textures, the idea ofproposing methods that reflect human perception arouses the enthusiasm ofresearchers in the field of image processing and computer vision. Therefore,the color space and the methods of analysis of color and texture, must bediscriminating to correspond to the human vision. Rock images are a typicalexample of natural images and their analysis is of major importance in the rockindustry. In this paper, we combine the statistical (Local Binary Pattern (LBP)with Hue Saturation Value (HSV) and Red Green Blue (RGB) color spaces fusion)and frequency (Gabor filter and Discrete Cosine Transform (DCT)) descriptorsnamed respectively Gabor Adjacent Local Binary Pattern Color Space Fusion(G-ALBPCSF) and DCT Adjacent Local Binary Pattern Color Space Fusion(D-ALBPCSF) for the extraction of visual textural and colorimetric featuresfrom direct view images of rocks. The textural images from the two G-ALBPCSFand D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2and the intersection of histograms that we have adapted to color histograms.The results obtained allowed us to highlight the discrimination of the rockclasses. The proposed extraction method provides better classification resultsfor various direct view rock texture images. Then it is validated by aconfusion matrix giving a low error rate of 0.8% of classification.

Global and local characterization of rock classification by Gabor and DCT filters with a color texture descriptor