  In classical sparse representation based classification and weighted SRC
algorithms, the test samples are sparely represented by all training samples.
They emphasize the sparsity of the coding coefficients but without considering
the local structure of the input data. To overcome the shortcoming, aiming at
the difficult problem of plant leaf recognition on the large-scale database, a
two-stage local similarity based classification learning method is proposed by
combining local mean-based classification method and local WSRC. In the first
stage, LMC is applied to coarsely classifying the test sample. 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
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 candidate neighbor subsets.
The rationale of the proposed method is as follows: the first stage aims to
eliminate the training samples that are far from the test sample and assume
that these samples have no effects on the ultimate classification decision,
then select the candidate neighbor subsets of the test sample. Thus the
classification problem becomes simple with fewer subsets; the second stage pays
more attention to those training samples of the candidate neighbor subsets in
weighted representing the test sample. This is helpful to accurately represent
the test sample. 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.
