A novel hybrid image segmentation method
Swarm intelligence algorithms have been extensively used in clustering-based applications, e.g., image segmentation, which is one of the fundamental components in image analysis and pattern recognition domains. Particle swarm optimization (PSO) is among swarm intelligence algorithms that perform based on population and random search. In this paper, a hybrid algorithm based on PSO, \(k\)-means, and learning automata is proposed for image segmentation. Each particle in the proposed method has been equipped with a learning automata (LA). In fact, each particle can either update its position by PSO method or select the next position utilizing \(k\)-means approach in each iteration based on its LA. In other word, the main aim of the hybrid proposed approach was to utilize the efficiency of PSO and \(k\)-mean methods under supervision of LA. The proposed approach along with other comparative studies has been applied for segmenting standard test images. Efficiency of the proposed method has been compared with that of other methods, and experimental results show the superiority proposed algorithm.
image segmentation particle swarm optimization learning automata data clustering multilevel thresholding