By Topic

Extracting features with structural skeleton framework for semantic image classification by using supporting vector machine

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

1 Author(s)
Chinpanthana, N. ; Fac. of Inf. Technol., Dhurakij Pundit Univ., Bangkok, Thailand

Searching images their semantic is an active problem in multimedia image retrieval. Many researchers have attempted to improve semantic models by using high-level concept based on keyword annotation. However, the annotation is tedious, in consistent, and erroneous. The retrieval process of such approaches is done by keyword searching. This model is rather rudimentary and it does not specific enough for representing the actual meaning. In this paper, we present a technique of the semantic image classification by using the human perception. The structural skeleton is used to extract the object components and image meaning. The feature selection methods are introduced to select the essential features from existing features. The experimental results indicate that our proposed approach offers significant performance improvements in the interpretation of semantic image classification, compare with other features, with the maximum of 93.80%.

Published in:

Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on  (Volume:3 )

Date of Conference:

20-22 Aug. 2010