Texture segmentation is one of the most important topic for Image analysis, understanding and interpretation. It is actually a topic where a lot of different approaches lead to more or less satisfying results. In general all of them try to match a particular feature or a feature vector which describes the analyzed region. Subsequently a threshold or threshold vector is applied and a texture class is assigned to the region. This paper describes how data mining algorithms can be used advantageously for texture based segmentation. Using a reference image with known texture, a model for a classifier is trained, that is applied to image regions of unknown texture. For the data mining it is necessary to calculate many different features and rate them (e.g by their information gain or correlation) accordingly. Only the best features selected this way are used to train a classifier, which is then used to segment subsequent images. Using this selected classifier it is possible to determine the location where a specific texture occurs in the image. The performance of the classifier is demonstrated for synthetic test images.

Keywords Texture segmentation, Texture, Image Processing, Image texture Analysis, Texture classification