Abstract
Aiming for the problem of inconsistent saliency between matched patches in person re-identification, a multi-directional salience similarity evaluation for person re-identification based on metric learning is proposed. A distribution analysis for salience consistency between the patches is taken, and the similarity between matched patches is established by weighted fusion of multi-directional salience. The weight of saliency in each direction is obtained using metric learning in the base of Structural SVM Ranking. It improves the discriminative and accuracy performance of re-identification. Compared with the similar algorithms, the method achieves higher re-identification rate with more comprehensive similarity measure.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (61104213), Natural Science Foundation of Jiangsu Province (BK2011146).
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Huo, Z., Chen, Y., Hua, C. (2015). Person Re-identification Based on Multi-directional Saliency Metric Learning. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_5
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DOI: https://doi.org/10.1007/978-3-319-20904-3_5
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