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Computational Intelligence and Neuroscience
Volume 2017, Article ID 9581292, 10 pages
Research Article

Discriminant WSRC for Large-Scale Plant Species Recognition

1Department of Information Engineering, Xijing University, Xi’an 710123, China
2Tableau Software, Seattle, WA 98103, USA

Correspondence should be addressed to Chuanlei Zhang; moc.liamg@74671a

Received 3 June 2017; Revised 3 November 2017; Accepted 15 November 2017; Published 25 December 2017

Academic Editor: Carlos M. Travieso-González

Copyright © 2017 Shanwen Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted.