Image Segmentation Using Electromagnetic Field Optimization (EFO) in E-Commerce Applications

2. Related Works

In recent past, various nature-inspired, bio-inspired and physics inspired optimization algorithms have been proposed for clustering and applied to image segmentation. In the last decade, significant progress in automatic clustering algorithms has been done. In particular, different evolutionary bio-inspired metaheuristics have been proposed to obtain near-optimal solutions for cluster analysis. These metaheuristics mimic the natural phenomenon of evolution, social behavior of swarms, physics-inspired behavior of objects. Bandyopadhyay et al. (2002) proposed a genetic algorithm with a variable length string. They used the Davies-Bouldin index as an objective function to evolve the clusters automatically and demonstrated the improved performance on real and artificial data sets. The proposed algorithm is named as GCUK for unknown k. (Omran et al., 2006) proposed particle swarm optimization-based clustering approach named as DCPSO. Initially, it partitioned data set into larger clusters and then used PSO for optimal cluster evolution for image segmentation. Mukhopadhyay & Maulik (2011) proposed a variable string length genetic fuzzy clustering-based image segmentation for T-1 and T-2 weighted brain MRI images.