Cluster Computing

, Volume 20, Issue 2, pp 1517–1525 | Cite as

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

  • Shanwen Zhang
  • Harry WangEmail author
  • Wenzhun Huang


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 \(k\times S\) 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.


Local similarity-based-classification learning (LSCL) Local mean-based clustering method (LMC) Weighted sparse representation based classification (WSRC) Local WSRC (LWSRC) Two-stage LSCL 



This work is partially supported by the China National Natural Science Foundation under Grant Nos. 61473237 and 61309008. It is also supported by the Shaanxi Natural Science Foundation Research Project under Grant No. 2014JM2-6096.The authors would like to thank all the editors and anonymous reviewers for their constructive advices.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Information EngineeringXijing UniversityXi’anChina
  2. 2.GoPerception LaboratoryIthacaUSA
  3. 3.Cornell UniversityIthacaUSA

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