1. A method for object detection, comprising: receiving an image and extracting features therefrom; applying a learning process to determine sub-regions and select predetermined pooling regions; performing selective max-pooling to choose one or more feature regions without noises, forming at least an object bounding box for a location; applying a cascaded boosting classifier to each object bounding box, with each weak classifier taking a feature response of a region inside the bounding box as its input and then the region is in tum represented by a group of small sub-regions (regionlets), and determining a permutation invariant feature operation on features extracted from regionlets as T ⁡ ( R ) = ∑ j = 1 N R ⁢ ⁢ α j ⁢ T ⁢ ( r j ) , ⁢ subject ⁢ ⁢ to ⁢ ⁢ α j ∈ { 0 , 1 } , ∑ j = 1 N R ⁢ ⁢ α j = 1 where T(R) as a feature representation for region R,T(r j ) as a feature extracted from the j th regionlet r j in R, N R is a total number of regionlets in region R, α j is a binary variable, either 0 or 1.