Rock Texture Retrieval Using Gray Level Co-occurrence Matrix

Nowadays, as the computational power increases, the role of automatic visual inspection becomes more important. Therefore, also visual quality control has gained in popularity. This paper presents an application of gray level co-occurrence matrix (GLCM) to texturebased similarity evaluation of rock images. Retrieval results were evaluated for two databases, one consisting of the whole images and the other with blocks obtained by splitting the original images. Retrieval results for both databases were obtained by calculating distance between the feature vector of the query image and other feature vectors in the database. Performance of the cooccurrence matrices was also compared to that of Gabor wavelet features. Co-occurrence matrices performed better for the given rock image dataset. This similarity evaluation application could reduce the cost of geological investigations by allowing improved accuracy in automatic rock sample selection.