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

Statistical image modeling for semantic 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

3 Author(s)
Zhongjie Zhu ; Ningbo Key Lab. of DSP, Zhejiang Wanli Univ., Ningbo, China ; Yuer Wang ; Gangyi Jiang

Semantic image segmentation (SIS) is one of the most crucial steps toward image understanding. In this paper, a novel framework to enable SIS is proposed by modeling images automatically. The statistical model for an image is automatically obtained by using a finite mixture model to approximate the underlying class distributions of image pixels. To accurately characterize the principal visual properties of the underlying dominant image compounds, a novel improved Expectation-Maximization (EM) algorithm is presented to select model structure and estimate model parameters simultaneously. Experiments were conducted and convincing results are obtained.

Published in:

Consumer Electronics, IEEE Transactions on  (Volume:56 ,  Issue: 2 )

Date of Publication:

May 2010

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.