Generalized Descriptor Compression for Storage and Matching

Smarter phones have made handheld computer vision a reality, but limited bandwidth, storage space and processing power prevent mobile phones from leveraging the full body of existing research. In particular, common techniques which use feature detectors and descriptors to solve problems in image matching and augmented reality cannot be used due to their space and processing requirements. We propose a general descriptor compression method which reduces descriptor size and provides fast descriptor matching without requiring decompression. By demonstrating how to apply our method to the commonly used SIFT, SURF and GLOH descriptors, we show its effectiveness in reducing size and inI’dcreasing accuracy. In all cases, we reduce the size of the descriptor by an order of magnitude and achieve higher accuracy at a detection rate of 95%.