  Segmentation partitions an image into different regions containing pixels
with similar attributes. A standard non-contextual variant of Fuzzy C-means
clustering algorithm (FCM), considering its simplicity is generally used in
image segmentation. Using FCM has its disadvantages like it is dependent on the
initial guess of the number of clusters and highly sensitive to noise.
Satisfactory visual segments cannot be obtained using FCM. Particle Swarm
Optimization (PSO) belongs to the class of evolutionary algorithms and has good
convergence speed and fewer parameters compared to Genetic Algorithms (GAs). An
optimized version of PSO can be combined with FCM to act as a proper
initializer for the algorithm thereby reducing its sensitivity to initial
guess. A hybrid PSO algorithm named Adaptive Particle Swarm Optimization (APSO)
which improves in the calculation of various hyper parameters like inertia
weight, learning factors over standard PSO, using insights from swarm
behaviour, leading to improvement in cluster quality can be used. This paper
presents a new image segmentation algorithm called Adaptive Particle Swarm
Optimization and Fuzzy C-means Clustering Algorithm (APSOF), which is based on
Adaptive Particle Swarm Optimization (APSO) and Fuzzy C-means clustering.
Experimental results show that APSOF algorithm has edge over FCM in correctly
identifying the optimum cluster centers, there by leading to accurate
classification of the image pixels. Hence, APSOF algorithm has superior
performance in comparison with classic Particle Swarm Optimization (PSO) and
Fuzzy C-means clustering algorithm (FCM) for image segmentation.
