Image Segmentation using Quantum Particle Swarm Optimization

This paper presents a novel image segmentation algorithm, which uses a biologically inspired paradigm known as swarm intelligence to segment images. A more efficient MRF based clustering algorithm that incorporated the Markov Random Field (MRF) theory and the Quantum Particle Swarm Optimization (QPSO) algorithm is proposed. the QPSO algorithm is ised to optimize the energy function which is a combinatorial optimization problem because of the large search space. The QPSO algorithm has few parameters and is simpler and more powerful than the classical version of the Particle Swarm Optimization algorithm. The experiments performed on both synthetic and real images show that the QPSO-MRF algorithm generates good results in clustering images and outperforms those obtained with other optimization methods.