Multiobjective fuzzy clustering with multiple spatial information for Noisy color image segmentation

Clustering method is a widely used and effective technique in color image segmentation. In general, traditional clustering-based image segmentation algorithms consider only one objective function and the segmentation performance is easily influenced by the noise in the image. Therefore, utilizing many clustering criteria and the neighborhood statistic information of pixels are more effective to improve the segmentation performance. In this paper, we put forward a multiobjective fuzzy clustering algorithm with multiple spatial information (MFCMSI) for noisy color image segmentation. Firstly, two conflicting fitness functions including the local spatial information with a circular neighborhood window and the non-local spatial information with circular search and similarity windows are designed to improve the noise-resistant ability and segmentation performance. In order to optimize these two fitness functions, the variable-length and cluster-center-based encoding strategy and some efficient evolutionary operations are utilized in each generation. Finally, a cluster validity index with multiple spatial information is constructed to select the best solution from the final non-dominated solution set of the last generation. Aiming to improve the robustness of MFCMSI, the ensemble strategy is introduced into MFCMSI and an ensemble version of MFCMSI is presented. Experimental results show that MFCMSI and its ensemble version behave well in evolving the number of segments and obtaining the satisfactory segmentation performance.