PAIRWISE ROTATIONAL-DIFFERENCE LBP FOR FINE-GRAINED LEAF IMAGE RETRIEVAL

In this study, we address the challenging issue of fine-grained leaf image retrieval which focuses on distinguishing different cultivars within the same species. We propose a novel local binary pattern, named pairwise rotation-difference LBP (PRDLBP), for the characterization of leaf image patterns. Different from the conventional LBP which measure the local grayscale contrast between the center pixel and its circular neighboring pixels, we consider the grayscale contrast between the circular neighboring pixels that are rotational symmetric about the center pixel. The proposed PRDLBP is a co-occurrence LBP feature representation which can not only encode spatially symmetric co-occurrence information, but also be inherently invariant to rotation. Its stronger discriminative power over the state-of-the-arts has been validated on two challenging fine-grained leaf image retrieval tasks, soybean cultivar identification and peanut cultivar identification. This work may attract considerable attention to fine-grained leaf image retrieval and advance the research of leaf image pattern identification from species to cultivars.