Abstract
Person re-identification is a challenging problem due to drastic variations in viewpoint, illumination and pose. Most previous works on metric learning learn a global distance metric to handle those variations. Different from them, we propose a view-adaptive metric learning (VAML) method, which adopts different metrics adaptively for different image pairs under varying views. Specifically, given a pair of images (or features extracted), VAML firstly estimates their view vectors (consisting of probabilities belonging to each view) respectively, and then adaptively generates a specific metric for these two images. To better achieve this goal, we elaborately encode the automatically estimated view vector into an augmented representation of the input feature, with which the distance can be analytically learned and simply computed. Furthermore, we also contribute a new large-scale multi-view pedestrian dataset containing 1000 subjects and 8 kinds of view-angles. Extensive experiments show that the proposed method achieves state-of-the-art performance on the public VIPeR dataset and the new dataset.
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Acknowledgement
The work is partially supported by Natural Science Foundation of China(NSFC) under contracts Nos. 61222211, 61272321, 61402430 and 61025010; and the China Postdoctoral Science Foundation 133366.
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Yan, C., Shan, S., Wang, D., Li, H., Chen, X. (2015). View-Adaptive Metric Learning for Multi-view Person Re-identification. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_46
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