Shift-variant Similarity Learning for Person Re-identification

Bing CHEN; Yufei ZHA; Yunqiang LI; Shengjie ZHANG; Yuanqiang ZHANG

doi:10.11999/JEIT180184

Volume 40 Issue 10

Sep. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(10): 2381-2387

Bing CHEN, Yufei ZHA, Yunqiang LI, Shengjie ZHANG, Yuanqiang ZHANG. Shift-variant Similarity Learning for Person Re-identification[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2381-2387. doi: 10.11999/JEIT180184

Citation:

Bing CHEN, Yufei ZHA, Yunqiang LI, Shengjie ZHANG, Yuanqiang ZHANG. Shift-variant Similarity Learning for Person Re-identification[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2381-2387. doi: 10.11999/JEIT180184

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Shift-variant Similarity Learning for Person Re-identification

doi: 10.11999/JEIT180184

Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, China

Funds: The National Natural Science Foundation of China (61472442, 61773397, 61701524), Shanxi Science and Technology New Star Fund (2015kjxx-46)

Received Date: 2018-02-09
Rev Recd Date: 2018-07-17

Available Online: 2018-07-23

Publish Date: 2018-10-01

Abstract

Abstract

The accuracy of pedestrian re-recognition mainly depends on the similarity measure and the feature learning model. The existing measurement methods have the characteristics of translation invariance, which make the training of network parameters difficult. Several existing feature learning models only emphasize the absolute distance between sample pairs, but ignore the relative distance between positive sample pairs and negative sample pairs, resulting in a weak discriminant feature in network learning. In view of the shortcomings of existing measurement methods, a distance measurement method of translation change is presented, which can effectively measure the similarity between images. To overcome the shortcomings of the feature learning model, based on the proposed translation distance metric, a new logistic regression model with enlarged intervals is proposed. By increasing the relative distance between the positive and negative sample pairs, the network can get more discriminant features. In the experiment, the validity of the proposed measurement and the feature learning model is verified on the Market1501, CUHK03 database. Experimental results show that the proposed metric performs better than the Mahalanobis distance metric 6.59%, and the proposed feature learning algorithm also achieves good performance. The average precision of the algorithm is improved significantly compared with the existing advanced algorithms.
- Person Re-identification,
- Shift-variant,
- Similarity learning,
- Logistic regression

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References(26)

References

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