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The steady increase of Internet in our society becomes more evident against the crude fact that more than a half of the internet infor125 Research in Computing Science 116 (2016) pp. 125–134; rec. 2016-03-21; acc. 2016-05-12 mation consists of images. This situation poses an open problem, how to recognize images automatically and unsupervised. The methodology for recognizing images by means of their own content (CBIR) has shown a lot of effectivity. CBIR resembles the orderly way in which humans manage to recognize images: seeing, analyzing and then classify them. In this work we present a solution to this problem by creating new statistical descriptors supporting the CBIR methodology. These are applied over images of natural scenes by means of Genetic Programming using a set of intuitive functions. This process involves the use of an amplitude function as a classifier of the self organization and the minimum distance of the attribute clusters. The incorporation of this last feature comes with a high computational cost. However, up to date, our results get a classification and recognition of almost 100 %.

Evolución de descriptores estadísticos de superficie de imágenes por programación genética para el reconocimiento de imágenes por CBIR: una primera aproximación