Learning Based Interactive Image Segmentation

I n this paper we present a n approach, to image segmentation in which user selected sets of examples and counter-examples supply information about the specific segmentation problem. 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 t o produce a candidate segmentation. The quality of each segmentation is evaluated within the genetic algorithm, b y a comparison of two physics-based techniques for region growing and edge detection. Experimental results o n real SAR imagery demonstrate that evolved segmentations are consistently better than segmentations derived f rom the Bayesian best single feature.