Mr. Bing Chen Profile

Bing Chen

at Air Force Engg Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 6 May 2019 Paper

Full batch loss for person re-identification

Bing Chen, Yufei Zha, Min Wu, Yunqiang Li, Zhiqiang Hou

Proceedings Volume 11069, 110690H (2019) https://doi.org/10.1117/12.2524336

KEYWORDS: Data modeling, Performance modeling, Statistical modeling, Databases, Mining, Cameras, Visualization, Convolutional neural networks, Network architectures, Lithium

Read Abstract +

Distance metric learning between pairs of samples is the key issue for the person re-identification(re-ID). Recently, convolutional neural networks (CNNs) significantly is employed to improve performance by learning deep semantic features which requires a large amount of labeled data for train the deep model. For person re-ID task, the training data is scarce. To the end, how to use the limited data to achieve the optimal deep model is a challenging problem. We observe that sample's similarity information and identity information are complementary. Therefore, a full-batch loss function is proposed in this paper, which promotes the distance relationship of individual sample pairs in a batch to the distance matrix in all samples to make full use of the sample similarity information. At the same time, it also makes full use of the sample's identity information by integrating the identification loss function. We conduct experiments on the two public person re-ID benchmarks: Market1501 and DukeMTMC-reID. The results clearly demonstrate that our proposed full batch loss model produces more discriminative descriptors for person re-ID, which outperforms well established baselines significantly and offer new state-of-the-art performances

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years