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Comparing texture feature sets for retrieving core images in petroleum applications

In this paper, the performance of similarity retrieval from a database of earth core images by using different sets of spatial and transformed-based texture features is evaluated and compared. A benchmark consisting of 69 core images from rock samples is devised for the experiments. We show that the Gabor feature set is far superior to other feature sets in terms of precision-recall for the benchmark images. This is in contrast to an earlier report by the authors in which we have observed that the spatial-based feature set outperforms the other feature sets by a wide margin for a benchmark image set consisting of satellite images when the evaluation window has to be small (32 X 32) in order to extract homogenous regions. Consequently, we conclude that optimal texture feature set for texture feature-based similarity retrieval is highly application dependent, and has to be carefully evaluated for each individual application scenario.