Learning-integrated interactive image segmentation

We present an approach to automatic image segmentation, in which user-selected sets of examples and counter-examples supply information about the specific segmentation problem. In our approach, image segmentation is guided by a genetic algorithm which learns the appropriate subset and spatial combination of a collection of discriminating functions, associated with image features. The genetic algorithm encodes discriminating functions into a functional template representation, which can be applied to the input image to produce a candidate segmentation. The performance of each candidate segmentation is evaluated within the genetic algorithm, by a comparison to two physics-based techniques for region growing and edge detection. Through the process of segmentation, evaluation, and recombination, the genetic algorithm optimizes functional template design efficiently. The contributions of this chapter include: genetic learning of functional template design, physics-based segmentation evaluation, novel crossover operator and fitness function, as well as a system prototype and experiments on real synthetic aperture radar (SAR) imagery of varying complexity.

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