  In the automatic classification of colored natural textures, the idea of
proposing methods that reflect human perception arouses the enthusiasm of
researchers in the field of image processing and computer vision. Therefore,
the color space and the methods of analysis of color and texture, must be
discriminating to correspond to the human vision. Rock images are a typical
example of natural images and their analysis is of major importance in the rock
industry. 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)) descriptors
named 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 features
from direct view images of rocks. The textural images from the two G-ALBPCSF
and D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2
and the intersection of histograms that we have adapted to color histograms.
The results obtained allowed us to highlight the discrimination of the rock
classes. The proposed extraction method provides better classification results
for various direct view rock texture images. Then it is validated by a
confusion matrix giving a low error rate of 0.8% of classification.
