SpringerOpen Newsletter

Receive periodic news and updates relating to SpringerOpen.

This article is part of the series Machine Learning in Image Processing.

Open Access Open Badges Research Article

Multiresolution Image Parametrization for Improving Texture Classification

Luka Šajn* and Igor Kononenko

Author Affiliations

Laboratory for Cognitive Modeling, Faculty of Computer and Information Science, University of Ljubljana, Tržaška 25, Ljubljana 1001, Slovenia

For all author emails, please log on.

EURASIP Journal on Advances in Signal Processing 2007, 2008:617457  doi:10.1155/2008/617457

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2008/1/617457

Received:1 September 2007
Accepted:3 December 2007

© 2008 The Author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the paper an innovative alternative to automatic image parametrization on multiple resolutions, based on texture description with specialized association rules, and image evaluation with machine learning methods is presented. The algorithm ArTex for parameterizing textures with association rules belonging to structural parametrization algorithms was developed. In order to improve the classification accuracy a multiresolution approach is used. The algorithm ARes for finding more informative resolutions based on the SIFT algorithm is described. The presented algorithms are evaluated on several public domains and the results are compared to other well-known parametrization algorithms belonging to statistical and spectral parametrization algorithms. Significant improvement of classification results was observed when combining parametrization attributes at several image resolutions for most parametrization algorithms. Our results show that multiresolution image parametrization should be considered when improvement of classification accuracy in textural domains is required. These resolutions have to be selected carefully and may depend on the domain itself.

Publisher note

To access the full article, please see PDF.