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

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.