By Topic

Use of Self-Organizing Maps for texture feature selection in content-based image retrieval

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
$33 $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)
Chen Guo ; Faculty of Information Technology at Monash University, Caulfield East, 3145, VIC, Australia ; Campbell Wilson

The ldquosemantic gaprdquo observed in content-based image retrieval (CBIR) has become a highly active research topic in last twenty years, and it is widely accepted that domain specification is one of the most effective methods of addressing this problem. However, along with the challenge of making a CBIR system specific to a particular domain comes the challenge of making those features object dependent. independent component analysis (ICA) is a powerful tool for detecting underlying texture features in images. However, features detected in this way often contain groups of features which are essentially shifted or rotated versions of each other. Thus, a method of dimensionality reduction that takes this self-similarity into account is required. In this paper, we proposed a self-organizing map (SOM) based clustering method to reduce the dimensionality of feature space. This method comprises two phases: clustering as well as representative selection. The result of the implementation confirms this method offers effective CBIR dimensionality reduction when using the ICA method of texture feature extraction.

Published in:

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Date of Conference:

1-8 June 2008