Video shot retrieval using a kernel derived from a continuous HMM

Atulya Velivelli; Thomas S. Huang; Alexander Hauptmann

doi:10.1117/12.650968

17 January 2006 Video shot retrieval using a kernel derived from a continuous HMM

Atulya Velivelli, Thomas S. Huang, Alexander Hauptmann

Proceedings Volume 6073, Multimedia Content Analysis, Management, and Retrieval 2006; 607311 (2006) https://doi.org/10.1117/12.650968
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

In this paper, we propose a discriminative approach for retrieval of video shots characterized by a sequential structure. The task of retrieving shots similar in content to a few positive example shots is more close to a binary classification problem. Hence, this task can be solved by a discriminative learning approach. For a content-based retrieval task the twin characteristics of rare positive example occurrence and a sequential structure in the positive examples make it attractive for us to use a learning approach based on a generative model like HMM. To make use of the positive aspects of both discriminative and generative models, we derive Fisher and Modified score kernels for a Continuous HMM and incorporate them into SVM classification framework. The training set video shots are used to learn SVM classifier. A test set video shot is ranked based on its proximity to the positive class side of hyperplane. We evaluate the performance of the derived kernels by retrieving video shots of airplane takeoff. The retrieval performance using the derived kernels is found to be much better compared to linear and RBF kernels.

Citation Download Citation

Atulya Velivelli, Thomas S. Huang, and Alexander Hauptmann "Video shot retrieval using a kernel derived from a continuous HMM", Proc. SPIE 6073, Multimedia Content Analysis, Management, and Retrieval 2006, 607311 (17 January 2006); https://doi.org/10.1117/12.650968

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