Evaluation of feature measures and similarity measures for content-based retrieval

Jiankang Wu; Chianprong Lam; G. Senthilkumar

doi:10.1117/12.227248

21 November 1995 Evaluation of feature measures and similarity measures for content-based retrieval

Jiankang Wu, Chianprong Lam, G. Senthilkumar

Proceedings Volume 2606, Digital Image Storage and Archiving Systems; (1995) https://doi.org/10.1117/12.227248
Event: Photonics East '95, 1995, Philadelphia, PA, United States

Abstract

Evaluation is a critical issue in any information systems. This problem has become more and more important with the rapid development of multimedia systems. Feature measures and similarity measures play a central role in content-based retrieval. Evaluation of their effectiveness and efficiency then become a key issue in assessing the performance of a content- based multimedia system. A learning algorithm has been studied to find a suitable and hopefully the best similarity function for a given set of feature measure and a given set of training data set.

Citation Download Citation

Jiankang Wu, Chianprong Lam, and G. Senthilkumar "Evaluation of feature measures and similarity measures for content-based retrieval", Proc. SPIE 2606, Digital Image Storage and Archiving Systems, (21 November 1995); https://doi.org/10.1117/12.227248

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