@inproceedings{10c2649ff7204b909a9ca8d3e80519c9,
title = "Person re-id by incorporating PCA loss in CNN",
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.",
author = "Kaixuan Zhang and Yang Xu and Li Sun and Song Qiu and Qingli Li",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018.; 24th International Conference on MultiMedia Modeling, MMM 2018 ; Conference date: 05-02-2018 Through 07-02-2018",
year = "2018",
doi = "10.1007/978-3-319-73600-6\_18",
language = "英语",
isbn = "9783319735993",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "200--212",
editor = "Ahmed Elgammal and Chalidabhongse, \{Thanarat H.\} and Supavadee Aramvith and Yo-Sung Ho and Klaus Schoeffmann and Ngo, \{Chong Wah\} and O'Connor, \{Noel E.\} and Moncef Gabbouj",
booktitle = "MultiMedia Modeling - 24th International Conference, MMM 2018, Proceedings",
address = "德国",
}