Journal of Shandong University(Engineering Science) ›› 2018, Vol. 48 ›› Issue (5): 16-23.doi: 10.6040/j.issn.1672-3961.0.2017.409

• Machine Learning & Data Mining • Previous Articles     Next Articles

Fast scene recognition based on LDB descriptor and local spatial structure matching

Dongbo ZHANG1,2(),Tao KOU1,2,Haixia XU1,2   

  1. 1. College of Information Engineering, Xiangtan University, Xiangtan 411105, Hunan, China
    2. Robot Visual Perception & Control Technology National Engineering Laboratory, Changsha 410012, Hunan, China
  • Received:2017-08-24 Online:2018-10-01 Published:2017-08-24
  • Supported by:
    国家自然科学基金资助项目(61602397);湖南省自然科学基金资助项目(2017JJ2251);湖南省重点学科建设资助项目

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Abstract:

A new local difference binary (LDB) descriptor and local spatial structure matching method was proposed to implement fast scene recognition. The multi-grid dense sampling method was used to obtain grayscale and gradient information of the image area, and the binary description was performed by comparing the grayscale and gradient size between the grids, which inherited the advantages of fast and low storage of binary feature extraction. The multi-point matching was adopted to replace the original single point of matching technology, which removed a large number of mismatches, thus the match accuracy was improved. The experiment showed that the efficiency of this method was about 2.7 times of SIFT and 1.9 times of SURF. The validity and recognition performance of the method were fully verified.

Key words: local feature, binary feature description, local spatial structure matching, scene identification, robust

CLC Number: 

  • TP391

Fig.1

Image Pyramid"

Fig.2

Schematic diagram of image patch centroids"

Fig.3

LDB feature calculation"

Fig.4

The matching image of the Civil Building and the Library Building by LDB descriptors"

Fig.5

The matching image of the Engineering Building by LDB descriptors"

Fig.6

Spatial structure extraction diagram"

Fig.7

Flow chart of structure matching judgment"

Fig.8

Sample images of the data set"

Fig.9

The matching results of several methods in different conditions"

Fig.10

The matching image of the Civil Building and the Library Building by our method"

Table 1

The matching results comparison of different scenarios with several algorithms"

方法 图片 关键点数 错误匹配点数
SIFT 土木楼 1 797 74
图书馆 2 151
SURF 土木楼 2 786 54
图书馆 2 340
ORB 土木楼 2 000 19
图书馆 2 000
LDB 土木楼 1 955 20
图书馆 1 925
本研究方法 土木楼 1 955 1
图书馆 1 925

Fig.11

The matching result of two images of the Engineering Buildings by our method"

Table 2

The statistical results comparison of matching the Engineering Buildings with several methods"

方法 图片 关键点数目 匹配点对 正确匹配数 准确率/%
SIFT 工科楼1 1 243 66 48 72.73
工科楼2 1 466
SURF 工科楼1 2 080 113 34 69.91
工科楼2 1 891
LDB 工科楼1 2 706 87 78 89.66
工科楼2 2 780
ORB 工科楼1 2 820 81 58 71.60
工科楼2 2 926
本研究 工科楼1 2 706 52 52 100
工科楼2 2 780

Table 3

Speed test results comparison of several methods"

方法 关键点总数 单张图片时间/s 单点时间/ms
Sift 2 790 2.94 1.08
Surf 3 971 3.05 0.77
ORB 3 949 0.79 0.20
本研究 3 664 1.48 0.40

Table 4

Recognition rate comparison of several methods"

方法 识别图片数量 正确识别数量 准确率/%
LDB 508 369 72.64
ORB 703 368 57.04
SIFT 241 216 86.53
SURF 197 171 86.80
本研究 304 299 98.36

Fig.12

The Recall-Precision contrast curves of the proposed method with GMS"

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