Mr. Zeming Feng Profile

Zeming Feng

at Univ of Jinan

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 | 16 February 2022 Paper

Subspace representation based on pairwise linear regression for large scale image set classification

Zeming Feng, Jiwen Dong, Xizhan Gao, Sijie Niu

Proceedings Volume 12083, 1208318 (2022) https://doi.org/10.1117/12.2623163

KEYWORDS: Image classification

Read Abstract +

In recent years, image set classification has attracted the attention of many scholars. It is mainly used in complex real scene classification, such as network short video classification, monitoring record classification, and multi-view camera network classification. Pairwise linear regression classification (PLRC) creatively introduces the unrelated subspace to increase the discriminative information, and it achieves a demonstrated better performance on image set classification. However, in the face of large scale image set classification, PLRC is not competent. One possible reason is that it is affected by too many outliers. In order to solve the problem of large scale image set classification, this paper propose two methods. Specifically, one using sparse subspace clustering to mine discriminative features, the other using the self-expressive function to extract exemplar from one image set, both of two methods using PLRC to finish the final classification task, which is not only conductive to reduce the impact of outliers, but also cuts down the computational burden of PLRC. Extensive experiments on two well-known databases prove that the performance of proposed algorithms is better than that of PLRC and several state-of-the-art classifiers.

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