By Topic

Entropy-based measures for clustering and SOM topology preservation applied to content-based image indexing and 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
$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)
Koskela, M. ; Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Finland ; Laaksonen, J. ; Oja, E.

Content-based image retrieval (CBIR) addresses the problem of finding images relevant to the users' information needs, based principally on low-level visual features for which automatic extraction methods are available. For the development of CBIR applications, an important issue is to have efficient and objective performance assessment methods for different features and techniques. In this paper, we study the efficiency of clustering methods for image indexing with entropy-based measures. Furthermore, the self-organizing map (SOM) as an indexing method is discussed further and an analysis method that takes into account also the spatial configuration of the data on the SOM is presented. The proposed methods enable computationally light measurement of indexing and retrieval performance for individual image features.

Published in:

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on  (Volume:2 )

Date of Conference:

23-26 Aug. 2004