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Segmentation partitions an image into different regions containing pixelswith similar attributes. A standard non-contextual variant of Fuzzy C-meansclustering algorithm (FCM), considering its simplicity is generally used inimage segmentation. Using FCM has its disadvantages like it is dependent on theinitial guess of the number of clusters and highly sensitive to noise.Satisfactory visual segments cannot be obtained using FCM. Particle SwarmOptimization (PSO) belongs to the class of evolutionary algorithms and has goodconvergence speed and fewer parameters compared to Genetic Algorithms (GAs). Anoptimized version of PSO can be combined with FCM to act as a properinitializer for the algorithm thereby reducing its sensitivity to initialguess. A hybrid PSO algorithm named Adaptive Particle Swarm Optimization (APSO)which improves in the calculation of various hyper parameters like inertiaweight, learning factors over standard PSO, using insights from swarmbehaviour, leading to improvement in cluster quality can be used. This paperpresents a new image segmentation algorithm called Adaptive Particle SwarmOptimization and Fuzzy C-means Clustering Algorithm (APSOF), which is based onAdaptive Particle Swarm Optimization (APSO) and Fuzzy C-means clustering.Experimental results show that APSOF algorithm has edge over FCM in correctlyidentifying the optimum cluster centers, there by leading to accurateclassification of the image pixels. Hence, APSOF algorithm has superiorperformance in comparison with classic Particle Swarm Optimization (PSO) andFuzzy C-means clustering algorithm (FCM) for image segmentation.

Color Image Segmentation using Adaptive Particle Swarm Optimization and Fuzzy C-means