1. A method, comprising: training groups of training features derived from regions of training images to obtain a plurality of classifiers, wherein each classifier corresponds to each group of training features, utilizing the plurality of classifiers to classify groups of validation features derived from regions of validation images to obtain a plurality of weights, wherein each weight corresponds to each region of the validation images and indicates how important the each region of the validation images is; and discarding a weight from the plurality of weights based upon a certain criterion, wherein the obtaining the plurality of weights further comprises: establishing a discriminant function η ⁡ ( z ) = log ⁢ P ⁡ ( t = 1 ❘ z ) P ⁡ ( t = 0 ❘ z ) = log ⁢ P ⁡ ( t = 1 ❘ z ) 1 - P ⁡ ( t = 1 ❘ z ) = β T ⁢ z , wherein P represents a probability, t=1 represents a validation image of the validation images positively matches to a training image of the training images, t=0 represents the validation image negatively matches to the training image, z represents how close the each region of the validation image is to the each region of the training image, β T represents the plurality of weights; establishing a likelihood function L ⁡ ( z ) = ∑ t ⁡ ( z i ) = 1 ⁢ η ⁢ ( z i ) - ∑ t ⁡ ( z j ) = 0 ⁢ ⁢ η ⁡ ( z j ) , wherein ∑ t ⁡ ( z i ) = 1 ⁢ η ⁢ ( z i ) represents a sum of discriminant functions for one or more of the validation images that positively match to one or more of the training images, and ∑ t ⁡ ( z j ) = 0 ⁢ ⁢ η ⁡ ( z j ) represents a sum of discriminant functions for one or more of the validation images that negatively match to one or more of the training images; and obtaining the plurality of weights by maximizing the likelihood function.