Cart (Loading....) | Create Account
Close category search window
 

Combined key-frame extraction and object-based video segmentation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Lijie Liu ; Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA ; Guoliang Fan

Video segmentation has been an important and challenging issue for many video applications. Usually there are two different video segmentation approaches, i.e., shot-based segmentation that uses a set of key-frames to represent a video shot and object-based segmentation that partitions a video shot into objects and background. Representing a video shot at different semantic levels, two segmentation processes are usually implemented separately or independently for video analysis. In this paper, we propose a new approach to combine two video segmentation techniques together. Specifically, a combined key-frame extraction and object-based segmentation method is developed based state-of-the-art video segmentation algorithms and statistical clustering approaches. On the one hand, shot-based segmentation can dramatically facilitate and enhance object-based segmentation by using key-frame extraction to select a few key-frames for statistical model training. On the other hand, object-based segmentation can be used to improve shot-based segmentation results by using model-based key-frame refinement. The proposed approach is able to integrate advantages of these two segmentation methods and provide a new combined shot-based and object-based framework for a variety of advanced video analysis tasks. Experimental results validate effectiveness and flexibility of the proposed video segmentation algorithm.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:15 ,  Issue: 7 )

Date of Publication:

July 2005

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.