@inproceedings{e95b4c657c97429ca839874edf83214c,
title = "Vehicle verification based on deep siamese network with similarity metric",
abstract = "Vehicle verification is a challenging research problem with important practical applications. Most prior work focused on either feature learning or distance metric learning, which could not guarantee the compatibility of the learned feature and the distance metric. In this paper, we propose an end-to-end model based on the Siamese Convolutional Neural Network (CNN), which integrates distance metric learning and feature learning into a unified framework. The network is trained by contrastive loss and a similarity metric loss defined by joint Bayesian to learn more discriminative features for vehicle verification. The experimental results demonstrate the effectiveness of the proposed method.",
keywords = "Joint bayesian, Siamese network, Similarity metric, Vehicle verification",
author = "Qian Zhang and Mingtao Pei and Mei Chen and Yunde Jia",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 18th Pacific-Rim Conference on Multimedia, PCM 2017 ; Conference date: 28-09-2017 Through 29-09-2017",
year = "2018",
doi = "10.1007/978-3-319-77380-3_74",
language = "English",
isbn = "9783319773797",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "773--782",
editor = "Bing Zeng and Hongliang Li and {El Saddik}, Abdulmotaleb and Xiaopeng Fan and Shuqiang Jiang and Qingming Huang",
booktitle = "Advances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers",
address = "Germany",
}