Abstract
This paper proposes an algorithm, particularly a loss function and its end to end learning manner, for person re-identification task. The main idea is to take full advantage of the labels in a batch during training, and to employ PCA to extract discriminative features. Deriving from the classic eigenvalue computation problem in PCA, our method incorporates an extra term in loss function with the purpose of minimizing those relative large eigenvalues. And the derivative with respect to the designed loss can be back-propagated in deep network by stochastic gradient descent (SGD). Experiments show the effectiveness of our algorithm on several re-id datasets.
K. Zhang and Y. Xu—Contributed equally to this work.
L. Sun—This work was supported in part by the National Natural Science Foundation of China under Project 61302125, 61671376 and in part by Natural Science Foundation of Shanghai under Project 17ZR1408500.
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Zhang, K., Xu, Y., Sun, L., Qiu, S., Li, Q. (2018). Person Re-id by Incorporating PCA Loss in CNN. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10705. Springer, Cham. https://doi.org/10.1007/978-3-319-73600-6_18
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