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The shapes of plant leaves are very important to plant ecologists and botanists because these can help distinguish plant species as well as serve as health indicators. In this paper, we present a novel contour-based shape descriptor named multiscale triangular centroid distance (MTCD) for plant leaf recognition. MTCD features at different triangles are extracted from each contour point to provide a compact, multiscale shape descriptor. Both local and global features of a plant leaf are effectively captured by the proposed method. A simple cosine distance is used to calculate the dissimilarity measurement between MTCD descriptors. Therefore, MTCD is a rapid approach for shape matching and is suitable for real-time application. The proposed method has been evaluated using four publicly available plant leaf datasets, including the Swedish Leaf dataset, the Smithsonian Leaf dataset, the Flavia Leaf dataset, and the ImageCLEF2012 Leaf dataset. The experimental results show that this novel approach can achieve high recognition accuracy. Comparisons with other state-of-the-art shape-based plant leaf recognition methods further demonstrate the effectiveness and efficiency of MTCD.
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