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An industrial rock classification system is constructed and studied. The local texture information in many image patches is extracted and classified. The decisions made at the local level are fused to form the high-level decision on the image/rock as a whole. The main difficulties of this application lay in significant variability and inhomogeneity of local textures caused by uneven rock surfaces and intrusions. Therefore, an emphasis is paid to the derivation of informative representation of local texture and to robust classification algorithms. The study focuses on the co-occurrence representation of texture comparing the two frequently used strategies, namely the approach based on Haralick features and methods utilizing directly the co-occurrence likelihoods. Apart of maximum-likelihood (ML) classifiers also an alternative method is studied considering the likelihoods to prototypes as feature of a new space. Unlike the ML methods, a classifier built in this space may leverage all training examples. It is experimentally illustrated, that in the rock classification setup the methods directly using the co-occurrence estimates outperform the feature-based techniques.

Improving the Maximum-Likelihood Co-occurrence Classifier: A Study on Classification of Inhomogeneous Rock Images