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

TL;DR

This paper introduces APSOF, a hybrid image segmentation method combining Adaptive Particle Swarm Optimization with Fuzzy C-means, which improves cluster center accuracy and handles noise better than traditional methods.

Contribution

The paper proposes a novel hybrid algorithm, APSOF, that enhances image segmentation by optimizing cluster initialization and reducing sensitivity to noise.

Findings

APSOF outperforms FCM in identifying accurate cluster centers.

APSOF achieves better segmentation quality than standard PSO and FCM.

Experimental results demonstrate improved classification accuracy.

Abstract

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.