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.

Plant leaf classification and retrieval using multi‐scale shape descriptor

“…It can be easily seen from Table 1 that our method achieves better classification performance than these notable descriptors. Specifically, the proposed method achieves 93.56% classification rate, which Unified method [28] 65.09 0.92…”

“…The aforementioned analysis and existing research results show that angle feature derived from contour points has good properties and is easy to extend to multi‐scale description [13, 21, 28, 29]. More specifically, angle feature is inherently rotation, translation, and scaling (RTS) invariant, and can capture hierarchical features from local contour variations to global information well in multi‐scale framework.…”

“…It is very easy to show that the extracted angle features θ are invariant to basic transformations like translation, scaling and rotation which facilitate matching between leaves [28]. However, if the starting point of C changes, the angle features θ will vary subsequently.…”

“…So R‐angle do not make the best of angle to capture leaf detail features, because radius is global description. In reference [28], a unified method is proposed to describe leaf multi‐scale geometric information, where a three‐step technique is used for multi‐scale generation. And experiments show excellent results on several datasets.…”

“…And each of the 1907 leaf images is used as a query leaf to compute the final MAP score and S-score. In addition to MARCH and MSRA, scores of other six state-ofthe-art approaches including Riemannian elastic metric, Unified method [28], TAR [27], MDM-RM [26], IDSC [29] and Shape Contexts are listed in Table 2 for the comparison, where "-" denotes that the data was not recorded in the references.…”