Retina-imitation sampling based binary descriptor - Qingsheng YUAN; Guoqing JIN; Dongming ZHANG; Xiuguo BAO (Tongxin xuebao)
The existing binary descriptors，generated from random or uniform point pairs sampling，suffer from low robustness and high computation.A novel sampling method,named RBS (retina-imitation based sampling),was proposed， which combines different densities sampling,multi-scale smoothing and reception field overlapping to imitate the converting from light signal to vision of ganglion cells of human retina cells,and further selects most discriminative comparison pairs based on learning on training data.Finally，compact binary descriptor was generated based on comparisons between the neighbor mean instead of singe sampled point.The experimental results show the RBS-128 with 128 bit outperforms FREAK and BRSIK with 512 bit about 16.4% and 5.3% in precision on the dataset provided by Mikolajczyk.