Abstract
Leaf recognition is one of the important techniques for species automatic identification. Because of not needing prior knowledge, clustering based method is a good choice to accomplish this task. Moreover, the high dimensions of leaf feature are always a challenge for traditional clustering algorithm. While the Sparse Subspace Clustering (SSC) can overcome the defect of traditional method in dealing with the high dimensional data. In this paper we propose to use SSC for leaf clustering. The experiments are performed on the database of leaves with noise and no noise respectively, and compared with some conventional algorithm such as k-means, k-medoids, etc. The results show that the clustering effect of SSC is more accurate and robust than others.
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Acknowledgments
This work was supported by the AnHui University Youth Skeleton Teacher Project(E12333010289), Anhui University Doctoral Scientific Research Start-up Funding(J10113190084), China Postdoctoral Science Foundation (2015M582826), Key Laboratory of Optical Calibration and Characterization/Chinese Academy of Sciences Project and Center of Information Support & Assurance Technology. In addition, this paper is partially supported by Science and Technology Project of Anhui Province (No. 1501b042207).
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Ding, Y., Yan, Q., Zhang, JJ., Xun, LN., Zheng, CH. (2016). Leaf Clustering Based on Sparse Subspace Clustering. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_5
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DOI: https://doi.org/10.1007/978-3-319-42294-7_5
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