Incorporating two first order moments into LBP-based operator for texture categorization

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Abstract

Within different techniques for texture modelling and recognition, local binary patterns and its variants have received much interest in recent years thanks to their low computational cost and high discrimination power. We propose a new texture description approach, whose principle is to extend the LBP representation from the local gray level to the regional distribution level. The region is represented by pre-defined structuring element, while the distribution is approximated using the two first statistical moments. Experimental results on four large texture databases, including Outex, KTH-TIPS 2b, CUReT and UIUC show that our approach significantly improves the performance of texture representation and classification with respect to comparable methods.

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APA

Nguyen, T. P., & Manzanera, A. (2015). Incorporating two first order moments into LBP-based operator for texture categorization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9008, pp. 527–540). Springer Verlag. https://doi.org/10.1007/978-3-319-16628-5_38

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