仿视网膜采样的二进制描述子

doi:10.11959/j.issn.1000-436x.2019021

Abstract

Abstract:

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.

Key words: binary descriptor, retina-imitation based sampling, ganglion cell, comparison pairs

CLC Number:

TP37

Qingsheng YUAN,Guoqing JIN,Dongming ZHANG,Xiuguo BAO. Retina-imitation sampling based binary descriptor[J]. Journal on Communications, 2019, 40(1): 15-23.

Figures/Tables 8

图像	变换	SIFT	ORB	BRISK	BRIEF	FREAK	RBS-32	RBS-64	RBS-128	RBS-160
	1-2	85.3%	64.1%	65.9%	56.9%	58.4%	62.3%	72.5%	79.7%	74.7%
	1-3	84.5%	63.1%	64.9%	58.6%	58.6%	60.4%	71.3%	79.7%	72.7%
wall	1-4	77.4%	53.2%	55.9%%	49.3%	48.5%	47.5%	63.1%	71.4%	64.9%
	1-5	66.3%	44.0%	46.7%%	36.7%	37.6%	41.1%	50.6%	59.7%	53.1%
	1-6	45.1%	30.1%	34.0%	23.0%	25.5%	30.1%	32.0%	33.2%	32.4%
	平均	71.7%	50.9%	53.5%	44.9%	45.7%	48.3%	57.9%	64.7%	59.5%
	1-2	77.2%	59.5%	60.5%	54.9%	57.6%	60.3%	62.4%	66.9%	62.9%
	1-3	62.6%	49.3%	50.4%	45.6%	48.8%	50.7%	53.1%	55.2%	54.0%
graffiti	1-4	45.3%	36.5%	36.5%	36.5%	33.9%	32.0%	34.7%	37.6%	35.4%
	1-5	39.5%	27.5%	29.6%	25.6%	24.8%	24.0%	22.9%	24.0%	22.6%
	1-6	22.2%	15.7%	16.8%	13.9%	10.4%	9.3%	12.3%	12.0%	11.8%
	平均	49.4%	37.7%	38.8%	35.3%	35.1%	35.3%	37.1%	39.1%	37.3%
	1-2	94.9%	93.6%	93.3%	91.9%	90.9%	90.5%	91.6%	93.2%	91.4%
	1-3	92.7%	83.8%	82.9%	81.7%	79.1%	75.9%	78.3%	81.7%	77.6%
ubc	1-4	89.2%	77.7%	78.6%	72.8%	71.9%	73.6%	76.8%	79.1%	76.6%
	1-5	85.8%	73.6%	77.4%	74.8%	73.6%	72.5%	73.0%	75.4%	73.7%
	1-6	83.9%	71.9%	74.5%	70.7%	68.1%	69.9%	71.0%	72.2%	70.7%
	平均	89.3%	80.1%	81.3%	78.4%	76.7%	76.5%	78.2%	80.3%	78.0%
	1-2	74.6%	57.6%	59.7%	52.0%	53.1%	58.7%	61.6%	65.1%	63.9%
	1-3	61.4%	51.5%	49.6%	44.0%	45.6%	48.5%	50.7%	52.3%	51.1%
boat	1-4	42.4%	33.1%	34.9%	36.0%	32.0%	29.3%	32.3%	33.9%	32.4%
	1-5	34.5%	24.8%	26.7%	23.2%	21.3%	20.3%	22.4%	22.9%	22.6%
	1-6	19.3%	14.1%	14.7%	11.2%	10.1%	7.5%	11.5%	11.5%	11.8%
	平均	46.4%	36.2%	37.1%	33.3%	32.4%	32.9%	35.7%	37.1%	36.4%
	1-2	91.5%	74.9%	77.9%	73.3%	71.2%	74.1%	77.6%	82.4%	76.6%
	1-3	90.1%	73.3%	76.0%	68.5%	67.7%	70.4%	75.7%	81.3%	76.6%
tree	1-4	85.1%	68.8%	71.2%	64.0%	64.8%	66.9%	72.3%	77.6%	74.7%
	1-5	79.5%	62.4%	64.8%	57.6%	59.7%	61.3%	66.7%	68.8%	66.8%
	1-6	69.0%	53.1%	56.0%	48.8%	50.4%	52.3%	54.9%	57.1%	54.0%
	平均	83.0%	66.5%	69.2%	62.5%	62.8%	65.0%	69.4%	73.4%	69.8%
	1-2	93.3%	82.7%	84.6%	78.7%	78.2%	79.8%	84.3%	87.0%	83.5%
	1-3	90.4%	77.7%	79.8%	73.1%	73.9%	75.0%	77.4%	81.4%	76.6%
leuven	1-4	87.2%	73.1%	74.2%	69.7%	68.9%	70.7%	73.9%	76.9%	73.7%
	1-5	84.6%	69.9%	71.5%	67.6%	66.2%	67.8%	71.8%	74.5%	72.7%
	1-6	81.2%	66.2%	66.0%	64.6%	61.4%	64.1%	66.0%	68.6%	64.9%
	平均	87.4%	73.9%	75.2%	70.7%	69.7%	71.5%	74.7%	77.7%	74.3%
平均	—	71.2%	57.6%	59.2%	54.2%	53.7%	54.9%	58.8%	62.1%	59.2%

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Retina-imitation sampling based binary descriptor

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比较内容	描述子总个数	总时间/ms	平均时间/μs
SIFT	2 415	3145	1302
ORB	1 516	76	50
BRISK	1 752	79	45
BRIEF	1 345	65	48
FREAK	1 428	51	36
RBS-32	1 456	15	10
RBS-64	1 456	30	21
RBS-128	1 456	60	41