This paper builds upon a previous texture feature selection and classification methodology by extending it with two state-of-the-art families of texture feature extraction methods, namely Manjunath & Ma's Gabor wavelet filters and Local Binary Pattern operators (LBP), which are integrated with more classical families of texture filters, such as co-occurrence matrices, Laws filters and wavelet transforms. Results with Brodatz compositions and outdoor images are evaluated and discussed, being the basis for a comparative study about the discrimination capabilities of those different families of texture methods, which have been traditionally applied on their own. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Melendez, J., Puig, D., & Garcia, M. A. (2007). Comparative evaluation of classical methods, optimized gabor filters and LBP for texture feature selection and classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4673 LNCS, pp. 912–920). Springer Verlag. https://doi.org/10.1007/978-3-540-74272-2_113
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