language: en

Generation of task-specific segmentation procedures as a model selection task

Ralf Herbrich and Tobias Scheffer
Technische Universitat Berlin, Artificial Intelligence Research Group,
Sekr. FR 5-8, Franklinstr. 28/29. D-10587 Berlin, Germany
email: ralfh|scheffer@cs.tu-berlin.de December 1, 1997

Abstract In image segmentation problems, there is usually a vast amount of filter operations available, a subset of which has to be selected and instantiated in order to obtain a satisfactory segmentation procedure for a particular domain. In supervised segmentation, a mapping from features, such as filter outputs for individual pixels, to classes is induced automatically. However, since the sample size required for supervised learning grows exponentially in the number of features it is not feasible to learn a segmentation procedure from a large amount of possible filters. But we argue that automatic model selection methods are able to select a region model in terms of some filters. We propose a wrapper algorithm that performs this task. We present results on artificial textured images (Brodatz) and report on our experiences with x-ray images.

Keywords: model based image segmentation, automatic model selection, learning pixel classifier, texture segmentation