PLBP: An effective local binary patterns texture descriptor with pyramid representation

Local binary pattern (LBP) is an effective texture descriptor which has successful applications in texture classification and face recognition. Many extensions are made for conventional LBP descriptors. One of the extensions is dominant local binary patterns which aim at extracting the dominant local structures in texture images. The second extension is representing LBP descriptors in Gabor transform domain (LGBP). The third extension is multi-resolution LBP (MLBP). Another extension is dynamic LBP for video texture extraction. In this paper, we extend the conventional local binary pattern to pyramid transform domain (PLBP). By cascading the LBP information of hierarchical spatial pyramids, PLBP descriptors take texture resolution variations into account. PLBP descriptors show their effectiveness for texture representation. Comprehensive comparisons are made for LBP, MLBP, LGBP, and PLBP. Performances of no sampling, partial sampling and spatial pyramid sampling approaches for the construction of PLBP texture descriptors are compared. The influences of pyramid generation approaches, and pyramid levels to PLBP based image categorization performances are discussed. Compared to the existing multi-resolution LBP descriptors, PLBP is with satisfactory performances and with low computational costs.

► PLBP is a general case of LBP, and with low computational cost.
► PLBP is performed by cascading LBP descriptors of hierarchical spatial pyramids.
► PLBP descriptors with wavelet show their effectiveness for texture representation.
► Different spatial sampling approaches of PLBP descriptors are discussed.