Figure-ground image segmentation using feature-based multi-objective genetic programming techniques

Yuyu Liang, Mengjie Zhang, Will N. Browne

Abstract: Figure-ground image segmentation is a process of separating regions of interest from a target image. Genetic programming has been employed to evolve segmentors that have the potential to capture high variations of images and conduct accurate segmentation. However, GP-based methods tend to evolve complex segmentors that have large sizes, are computationally expensive and difficult to interpret. Therefore, this work aims to balance the solution functionality with the complexity by applying multi-objective techniques to GP. Specifically, NSGA-II (nondominated sorting genetic algorithm) and SPEA2 (strength Pareto evolutionary algorithm) are selected as the base multi-objective techniques, in which a new Pareto dominance mechanism is designed, thus creating two new multi-objective techniques—INSGA-II (improved NSGA-II) and ISPEA2 (improved SPEA2). By applying the INSGA-II and ISPEA2 to GP, respectively, two novel multi-objective GP (MOGP) methods are proposed—INSGP and ISPGP. Both methods have two objectives: a solution functionality measure (i.e. the classification accuracy) and a solution complexity measure based on an exponential function. The results show that the proposed MOGP methods can evolve solutions with good trade-offs between the functionality and complexity, and INSGP is better at keeping solution diversity than ISPGP for the segmentation tasks in this paper. Moreover, the analyses on the evolved segmentors show that certain discriminatory patterns can be captured.