Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Comparison of image partition methods for adaptive image categorization based on structural image representation

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)
Zhiyong Wang ; Sch. of Inf. Technol., Sydney Univ., NSW, Australia ; Feng, D. ; Zheru Chi

Image categorization is very helpful for organizing large image databases efficiently, however, it is yet very challenging due to lack of effective image representations. Our previous work showed that structural representations were good at characterizing image contents, since image contents could be exploited from coarse to fine scales through the structures representation and fewer visual features are required. In this paper, several popular image partition methods are investigated for adaptive image categorization based on structural representation. Experimental results on seven categories of scenery images show that both the structure and node attributes are important to categorize image contents. In addition, the more similar the structures of each category, the better the categorization performance.

Published in:

Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th  (Volume:1 )

Date of Conference:

6-9 Dec. 2004