Incorporating negative examples in one-class SVM for relevance feedback in image retrieval

Hong Wu; Hanqing Lu; Songde Ma

doi:10.1117/12.477119

31 July 2002 Incorporating negative examples in one-class SVM for relevance feedback in image retrieval

Hong Wu, Hanqing Lu, Songde Ma

Proceedings Volume 4875, Second International Conference on Image and Graphics; (2002) https://doi.org/10.1117/12.477119
Event: Second International Conference on Image and Graphics, 2002, Hefei, China

Abstract

Relevance feedback has been proven to be an effective scheme in content-based image retrieval to improve retrieval performance. Recently, SVM based techniques are introduced into the learning process of relevance feedback, for its good generalization ability in a high dimensional space in condition of small example size. Based on the extended one-class SVM that can handle negative examples, we propose a new relevance feedback scheme to overcome the limitation of the recent methods. The scheme is flexible to handle the situations with and without negative examples; and can further improve the retrieval performance when negative examples provided. The new scheme was evaluated on a database of 6,000 images and compared to previous methods. Experimental results have demonstrated its effectiveness.

Citation Download Citation

Hong Wu, Hanqing Lu, and Songde Ma "Incorporating negative examples in one-class SVM for relevance feedback in image retrieval", Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); https://doi.org/10.1117/12.477119

ACCESS THE FULL ARTICLE

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