Evolving semantic object segmentation methods automatically by genetic programming from images and image processing operators.

Even though numerous segmentation methods exist, the requirement of prior knowledge or parameter tuning makes them restricted to limited image domains. Without predefining solution models, genetic programming (GP) is able to solve complex problems by evolving computer programs automatically. In this paper, three new GP-based methods are designed to evolve segmentation algorithms automatically from images and primitive image processing operators (e.g., filters and histogram equalization). Specifically, a strongly typed representation, the cooperative coevolution technique and a two-stage evolution are introduced in GP, respectively, to form three new methods that can evolve solutions to conduct image preprocessing, segmentation and postprocessing automatically. The new methods are termed as StronglyGP, CoevoGP and TwostageGP, and standard GP-based algorithm (StandardGP) is employed as a reference method. The proposed methods are tested on two complicated datasets (i.e., Weizmann and Pascal datasets), which contain high variations in both objects and backgrounds. The results show that StronglyGP and StandardGP can evolve effective segmentors for the given complex segmentation tasks, while CoevoGP and TwostageGP perform worse than StronglyGP and StandardGP, which may be caused by the overfitting problem in deriving postprocessing solutions. In addition, compared with StandardGP, StronglyGP achieves better segmentation performance with smaller solution sizes. Moreover, compared with four widely used segmentation methods, StronglyGP and StandardGP can produce satisfactory results consistently on both Weizmann and Pascal datasets. [ABSTRACT FROM AUTHOR]