基于指数权重局部聚合向量特征的轮毂型号识别

doi:10.3969/j.issn.1000-1158.2019.04.20

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Abstract In the process of wheel model identification, in order to quickly identify the correct wheel model in a large number of wheel model data base, a identification algorithm based on index weight VLAD(vector of aggragate locally descriptor) is proposed. The VLAD feature is an improved version of the BOW, using the cumulative residuals between features and cluster centers replace the cumulative number of features, as the searching algorithm we use four nearest neighbor soft allocation, and has better robustness than the one to one allocation rule. Then the PCA is used to reduce dimensions of VLAD obtained above, the multiply index weight and the data of descending dimension VLAD to reduce individual instability value, and finally, the most similar pictures are found by comparing the feature vectors. The experimental results shows that this algorithm has the advantages of non-contact, perform well on flexibility and accuracy, it also improves the identification rate and has good robustness.

Key words： metrology wheel model identification vector of aggragate locally descriptor clustering principal component analysis of dimension reduction index weight

Received: 26 March 2018 Published: 10 June 2019

PACS:

TB96

Corresponding Authors: CHENG Shuhong E-mail: shhcheng@ysu.edu.cn

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	Articles by authors
	ZHANG Dian-fan
	GUAN Yong-lai
	ZHANG Li
	CHENG Shu-hong

Cite this article:

ZHANG Dian-fan,GUAN Yong-lai,ZHANG Li, et al. Wheel Model Identification Based on Index Weight Vector of Aggragate Locally Descriptor Features[J]. Acta Metrologica Sinica, 2019, 40(4): 670-675.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.04.20 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I4/670

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