1. A computer-implemented method for image classification, comprising: a. coding an image by nonlinearly mapping an image descriptor to form a high-dimensional sparse vector; b. spatially pooling each local region to form an image-level feature vector using a probability kernel incorporating a similarity metric of local descriptors; and c. classifying the image; wherein the nonlinear mapping of f(x) is approximated by a linear function w T φ(x), where φ(x) is called the Super-Vector (SV) coding of x, defined by 
 φ( x )=[ sγ v ( x ),γ v ( x )( x−v ) T ] vεC T , and wherein the spatially pooling forms a modified Bhattacharyya kernel.