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In classical sparse representation based classification and weighted SRCalgorithms, the test samples are sparely represented by all training samples.They emphasize the sparsity of the coding coefficients but without consideringthe local structure of the input data. To overcome the shortcoming, aiming atthe difficult problem of plant leaf recognition on the large-scale database, atwo-stage local similarity based classification learning method is proposed bycombining local mean-based classification method and local WSRC. In the firststage, LMC is applied to coarsely classifying the test sample. nearestneighbors of the test sample, as a neighbor subset, is selected from eachtraining class, then the local geometric center of each class is calculated. Scandidate neighbor subsets of the test sample are determined with the firstsmallest distances between the test sample and each local geometric center. Inthe second stage, LWSRC is proposed to approximately represent the test samplethrough a linear weighted sum of all samples of the candidate neighbor subsets.The rationale of the proposed method is as follows: the first stage aims toeliminate the training samples that are far from the test sample and assumethat these samples have no effects on the ultimate classification decision,then select the candidate neighbor subsets of the test sample. Thus theclassification problem becomes simple with fewer subsets; the second stage paysmore attention to those training samples of the candidate neighbor subsets inweighted representing the test sample. This is helpful to accurately representthe test sample. Experimental results on the leaf image database demonstratethat the proposed method not only has a high accuracy and low time cost, butalso can be clearly interpreted.

Two-stage Plant Species Recognition by Combining Local K-NN and Weighted Sparse Representation