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Efficient levels of spatial pyramid representation for local binary patterns

Efficient levels of spatial pyramid representation for local binary patterns

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Local binary patterns (LBPs) are a well-known operator that shows the ability for rotation and scale invariant texture classification. A recent extension of this operator is the pyramid transform domain approach on LBPs (PLBP). Obtaining more accuracy by using more pyramid representations is an important result of PLBP, which increases not only feature dimensionality, but also classification computational time (CT). This study illustrates that more pyramid image representations will not improve the performance of the PLBP. We evaluate efficient levels of representation for the PLBP descriptor. In addition, the authors propose some feature selection approaches, such as the multi-level and multi-resolution (ML + MR) approach and the ML, MR and multi-band (ML + MR + MB) approach and discuss their efficiency and CT. Experimental results show that the proposed feature selection approaches improve the accuracy of texture classification with fewer pyramid image representations. In addition, replacing the Chi-2 similarity measurement with Czekannowski improves the accuracy of texture classification.

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