Journal
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
Volume 20, Issue 2, Pages 1517-1525Publisher
SPRINGER
DOI: 10.1007/s10586-017-0859-7
Keywords
Local similarity-based-classification learning (LSCL); Local mean-based clustering method (LMC); Weighted sparse representation based classification (WSRC); Local WSRC (LWSRC); Two-stage LSCL
Funding
- China National Natural Science Foundation [61473237, 61309008]
- Shaanxi Natural Science Foundation Research Project [2014JM2-6096]
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Aiming at the difficult problem of plant leaf recognition on the large-scale database, a two-stage local similarity based classification learning (LSCL) method is proposed by combining local mean-based clustering (LMC) method and local sparse representation based classification (SRC) (LWSRC). In the first stage, LMC is applied to coarsely classifying the test sample. k nearest neighbors of the test sample, as a neighbor subset, is selected from each training class, then the local geometric center of each class is calculated. S candidate neighbor subsets of the test sample are determined with the first S smallest distances between the test sample and each local geometric center. In the second stage, LWSRC is proposed to approximately represent the test sample through a linear weighted sum of all samples of the S candidate neighbor subsets. Experimental results on the leaf image database demonstrate that the proposed method not only has a high accuracy and low time cost, but also can be clearly interpreted.
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