MARCH: Multiscale-arch-height description for mobile retrieval of leaf images
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In this paper, we propose a novel shape description method for mobile retrieval of leaf images. In this method, termed multiscale arch height (MARCH), hierarchical arch height features at different chord spans are extracted from each contour point to provide a compact, multiscale shape descriptor. Both the global and detailed features of the leaf shape can be effectively captured by the proposed algorithm. MARCH descriptors are compared using a simple L1-norm based dissimilarity measurement providing very fast shape matching. The algorithm has been tested on four publicly available leaf image datasets including the Swedish leaf dataset, the Flavia leaf dataset, the ICL leaf dataset and the scanned subset of the ImageCLEF leaf dataset. The experiments indicate that the proposed method can achieve a higher classification rate and retrieval accuracy than the state-of-the-art benchmarks with a more than 500 times faster retrieval speed. A mobile retrieval system embedding the proposed algorithms has been developed for the real application of leaf image retrieval.
Pattern Recognition and Data Mining