1. An image classification method, comprising: receiving an overcomplete set of spatial regions; jointly optimizing the classifier and the pooling region for each pooled feature; performing incremental feature selection and retraining using a grafting process to efficiently train the classifier, and training a multiple-class linear classifier y=f(x)=Wx+b with an optimization problem: min W , b ⁢ 1 N ⁢ ∑ n = 1 N ⁢ l ⁡ ( W T ⁢ x n + b , y n ) + λ 1 1 ⁢  W  Fro 2 + λ 2 ⁢  W  1 , ∞ where Y i is the L-dimensional label vector coded in a 1-of-L fashion, with values taken from {−1,+1} given L classes x i is an M-dimensional feature vector defined by overcomplete pooling in the previous subsection, and W=[w 1 , w 2 ,. .. , w L ] is a M×L weight matrix containing the weight vector for the L classifiers.