In the research community of content-based image retrieval, great success has been achieved for leaf image retrieval in species. However, little progress has been made on the more challenging fine-grained leaf image retrieval (FGLIR) which focuses on subspecies/cultivars recognition. To address it, a novel co-occurrence local binary pattern (CoLBP), named Multi-scale Binary-Pattern Dependency (MBPD), is proposed in this study. Despite the potential of CoLBP in encoding contextual information among texture patterns, how to correlate LBPs to yield discriminative co-occurrence features remains an open issue. We introduce two new concepts, Axisymmetric Co-occurrence (ACO) and Cross-thresholding (CRT), into the design of CoLBP. The ACO produces CoLBP by sliding a pair of axisymmetric lines over an adaptive local patch to capture spatial axisymmetric relationship among LBPs. While the CRT correlates LBPs in intensity domain through exchanging their respective thresholds. Their combination is used to yield ACO-CRT local descriptors to encode the dependency information in both spatial and intensity domains. The ACO-CRT local descriptors of multi-scale and multi-position are aggregated into MBPD representation for efficient dissimilarity measure between two leaf images. Extensive experiments validate the superior performance of our method over the state-of-the-arts on FGLIR.