Towards symbolic mining of images with association rules: Preliminary results on textures

Guest editors: Paula Britox and Monique Noirhomme-Fraiturey

Article type: Research Article

Authors: Bevk, Matjaž | Kononenko, Igor

Affiliations: Faculty of Computer and Information Science, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia. E-mail: [email protected], [email protected] | [x] Faculdade de Economia, University of Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal. E-mail: [email protected] | [y] Institut d'Informatique, Facultés Universitaires Notre Dame de la Paix, Rue Grandgagnage, 21, B-5000 Namur, Belgium. E-mail: [email protected]

Abstract: This paper presents new textural features which are based on association rules. We give a texture representation, which is an appropriate formalism, that allows straightforward application of association rules algorithms. This representation has several good properties like invariance to global lightness and invariance to rotation. Association rules capture structural and statistical information and are very convenient to identify the structures that occur most frequently and have the most discriminative power. The results from our experiments show that this representation gives comparable results to standard texture descriptions and better results than general image descriptions.

DOI: 10.3233/IDA-2006-10406

Journal: Intelligent Data Analysis, vol. 10, no. 4, pp. 379-393, 2006