Efficient image representation for object recognition via pivots selection

摘要

Patch-level features are essential for achieving good performance in computer vision tasks. Besides wellknown pre-defined patch-level descriptors such as scaleinvariant feature transform (SIFT) and histogram of oriented gradient (HOG), the kernel descriptor (KD) method [1] offers a new way to “grow-up” features from a match-kernel defined over image patch pairs using kernel principal component analysis (KPCA) and yields impressive results.