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

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 fo r a particular domain. In supervised segmentation, a mapping from features, such as filter outputs for individual pixels, to classes is induced automa tically. However, since the sample size required for supervised learning grow s exponentially in the number of features it is not feasible to learn a segment ation procedure from a large amount of possible filters. But we argue that auto matic model selection methods are able to select a region model in terms of some filters.