Techniques for designing a classifier for multimedia indexing

Ankush Mittal; Loong-Fah Cheong

doi:10.1117/12.410919

1 January 2001 Techniques for designing a classifier for multimedia indexing

Ankush Mittal, Loong-Fah Cheong

Proceedings Volume 4315, Storage and Retrieval for Media Databases 2001; (2001) https://doi.org/10.1117/12.410919
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

Abstract

This paper addresses the issues involved in designing a classifier for multimedia indexing, a representative of domain of tasks involving high dimensionality of feature space and large dissimilarity between features in range and variation, and requiring a strong inference mechanisms. We consider decision trees, Bayesian network, neural network and support vector approaches. The Modified Bayesian Network (MBN), as designed by us offers significant advantages over other approaches. The application of Bayesian network has generally been restricted to domains having discrete variable values, or to the domain with continuos variable values which approximate to Gaussian distribution. However, MBN can form sound representation of non-Gaussian Multimodal continuous distribution, as is the case with feature space in multimedia indexing. This can be accomplished by intelligent partitioning and data clique association. The structure of MBN and its functionality on real video is also presented in the paper.

Citation Download Citation

Ankush Mittal and Loong-Fah Cheong "Techniques for designing a classifier for multimedia indexing", Proc. SPIE 4315, Storage and Retrieval for Media Databases 2001, (1 January 2001); https://doi.org/10.1117/12.410919

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