Multi-objective nature-inspired clustering and classification techniques for image segmentation
This paper aims to provide a comprehensive review of nature-inspired techniques used in image segmentation problems. We focus particularly on multi-objective clustering and classification approaches. The approaches are classified based on the various aspects of optimization, various possible problem formulations, and types of datasets modeled. In the multi-objective clustering methods, the definition of the types of representation methods, encoding techniques, and number of clusters defined (fixed/variable) are presented. In the use of multi-objective nature-inspired techniques in classification, we describe issues related to diversity measures, accuracy measures, rule manipulation, and managing uncertainties. Through our analysis of the current state of research, we hope to address important challenges and provide specific directions for future modeling of similar problems with multi-objective optimization techniques.
clustering image segmentation nature-inspired technique multi-objective optimization classifier design