A multi-objective approach based on TOPSIS to solve the image segmentation combination problem

Abstract

Recently, there has been renewed interest in the fusion of image segmentation. However, previous relevant research has been impeded by the lack of an appropriate single segmentation criterion, which yields an improved final segmentation result. This paper proposes a new framework to tackle this problem. It is based on multi-objective optimization strategy, followed by a decision making technique called: technique for order performance by similarity to ideal solution (TOPSIS). This new fusion framework aims to overcome the limits caused by using a single criterion by combining and optimizing, simultaneously, two different and complementary segmentation criteria; namely, the global consistency error (GCE) (region-based criterion) and the F-measure (edge-based criterion). This new multi-criterion fusion framework is validated on the Berkeley image dataset and compared to different segmentation algorithms (with or without fusion strategy). Experiments show that the results of our new multi-objective approach improve the state of the art in terms of popular indices.