Abstract
In this paper, a multi-objective image segmentation approach with an Interactive Evolutionary Computation (IEC)-based framework is presented. Two objectives, i.e., the overall deviation and the connectivity measure, are optimized simultaneously using a multi-objective evolutionary algorithm to generate parameters used for segmentation. In addition, an IEC framework to allow users to participate in the parameters optimization process directly is devised. To demonstrate the effectiveness of the proposed IEC-based multi-objective image segmentation approach, a series of experiments is conducted, and the results are compared with those from other segmentation methods. The outcomes ascertain that the proposed approach is effective, as it compares favorably with other classical approaches.