In
geology, classification and lithological recognition of rocks plays an
important role in the area of oil and gas exploration, mineral exploration and
geological analysis. In other fields of activity such as construction and
decoration, this classification makes sense and fully plays its role. However,
this classification is slow, approximate and subjective. Automatic
classification curbs this subjectivity and fills this gap by offering methods
that reflect human perception. We propose a new approach to rock classification
based on direct-view images of rocks. The aim is to take advantage of feature
extraction methods to estimate a rock dictionary. In this work, we have
developed a classification method obtained by concatenating four (4) K-SVD
variants into a single signature. This method is based on the K-SVD algorithm
combined with four (4) feature extraction techniques: DCT, Gabor filters,
D-ALBPCSF and G-ALBPCSF, resulting in the four (4) variants named K-Gabor,
K-DCT, KD-ALBPCSF and KD-ALBPCSF respectively. In this work, we developed a
classification method obtained by concatenating four (4) variants of K-SVD. The
performance of our method was evaluated on the basis of performance indicators
such as accuracy with other 96% success rate.
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