Unified multi‐scale method for fast leaf classification and retrieval using geometric information

Abstract

Leaf image identification is a significant and challenging research work. Here, a unified multi-scale method is proposed to capture leaf geometric information for plant leaf classification and image retrieval. For each point on the leaf contour, the unified multi-scale method utilises a simple yet effective three-step strategy to locate corresponding neighbour points. The descriptor extracted using these neighbour points can provide a coarse-to-fine description of leaf contours and is of multi-scale characteristic intrinsically. More importantly, there is no scale parameter to be adjusted in the method, and hence no optimisation procedure is required. The proposed method is applied to three well-known contour features to capture geometric information of leaves, including angle, arch-height, and triangle-area representation. FFT is applied on the features in unified multi-scale method for convenient and fast leaf matching. Leaf classification and image retrieval experiments are conducted on four challenging leaf datasets to test the proposed method and evaluated using three standard performance evaluation metrics. The experimental results and comparisons with the state-of-art methods indicate that the unified multi-scale method has remarkable performance.