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

TL;DR

This paper introduces combined statistical and frequency-based descriptors for improved automatic classification of natural rock textures, achieving high accuracy and low error rates by reflecting human visual perception.

Contribution

It proposes novel fusion descriptors combining LBP, Gabor, and DCT in different color spaces for better rock image classification, validated with low error rates.

Findings

Achieved 0.8% classification error rate

Enhanced discrimination of rock classes

Validated effectiveness with similarity metrics and confusion matrix

Abstract

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.