Abstract
Texture-based pixel classification has been traditionally carried out by applying texture feature extraction methods that belong to a same family (e.g., Gabor filters). However, recent work has shown that such classification tasks can be significantly improved if multiple texture methods from different families are properly integrated. In this line, this paper proposes a new selection scheme that automatically determines a subset of those methods whose integration produces classification results similar to those obtained by integrating all the available methods but at a lower computational cost. Experiments with real complex images show that the proposed selection scheme achieves better results than well-known feature selection algorithms, and that the final classifier outperforms recognized texture classifiers.
This work has been partially supported by the Spanish Ministry of Science and Technology under project DPI2004-07993-C03-03.
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Puig, D., Garcia, M.Á. (2005). Automatic Selection of Multiple Texture Feature Extraction Methods for Texture Pattern Classification. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_27
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DOI: https://doi.org/10.1007/11492542_27
Publisher Name: Springer, Berlin, Heidelberg
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