A novel supervised approach to learning efficient kernel descriptors for high accuracy object recognition

摘要

Discriminative patch-level features are essential for achieving good performance in many computer vision tasks. Recently, unsupervised learning approaches have been employed to design such features based on the similarities of image patches. These approaches, such as kernel descriptors (KD) and efficient kernel descriptors (EKD), have shown superior performance than pre-defined image features (e.g., SIFT or HoG) in object recognition. They gave a kernel generalization of orientation histograms and suggested a promising way to 'grow-up' features based on available information.