Abstract
Person re-identification is a challenging issue due to large visual appearance changes caused by variations in viewpoint, lighting, background clutter and occlusion among different cameras. Recently, Mahalanobis metric learning methods, which aim to find a global, linear transformation of the feature space between cameras [1–4], are widely used in person re-identification. In order to maximize the inter-class variation, general Mahalanobis metric learning methods usually push impostors (i.e., all negative samples that are nearer than the target neighbors) to a fixed threshold distance away, treating all these impostors equally without considering their diversity. However, for person re-identification, the discrepancies among impostors are useful for refining the ranking list. Motivated by this observation, we propose an Adaptive Margin Nearest Neighbor (AMNN) method for person re-identification. AMNN aims to take unequal treatment to each samples impostors by pushing them to adaptive variable margins away. Extensive comparative experiments conducted on two standard datasets have confirmed the superiority of the proposed method.
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Acknowledgement
The research was supported by National Nature Science Foundation of China (No. 61231015, No. 61170023, No. 61172173, No. 61303114). National High Technology Research and Development Program of China (863 Program, No. 2015AA016306). Technology Research Program of Ministry of Public Security (No. 2014JSYJA016). The EUFP7 QUICK project under Grant Agreement (No. PIRSES-GA-2013-612652). Major Science and Technology Innovation Plan of Hubei Province (No. 2013AAA020). Internet of Things Development Funding Project of Ministry of industry in 2013 (No. 25). China Postdoctoral Science Foundation funded project (2013M530350). Specialized Research Fund for the Doctoral Program of Higher Education (No. 20130141120024). Nature Science Foundation of Hubei Province (2014CFB712). The Fundamental Research Funds for the Central Universities (2042014kf0250, 2014211020203). Jiangxi Youth Science Foundation of China(Grant No. 20151BAB217013).
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Yao, L. et al. (2015). Adaptive Margin Nearest Neighbor for Person Re-Identification. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_8
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DOI: https://doi.org/10.1007/978-3-319-24075-6_8
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