Two-stage plant species recognition by local mean clustering and Weighted sparse representation classification

• Published in: Cluster computing 2017-06, Vol.20 (2), p.1517-1525
• Main Authors: Zhang, Shanwen, Wang, Harry, Huang, Wenzhun
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Classification; Cluster analysis; Clustering; Computer Communication Networks; Computer Science; Dictionaries; Discriminant analysis; Flowers & plants; Leaves; Methods; Operating Systems; Pattern recognition; Plants (botany); Processor Architectures; Recognition; Representations; Sparsity
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-017-0859-7

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 k × S 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.