Title :
A new image segmentation hybrid algorithm
Author :
Yongfeng Xu ; Bo Zhang ; Yongli Su
Author_Institution :
Dept. of Mathematic, Northwest Univ., Xi´´an, China
Abstract :
A new hybrid algorithm for image segmentation based on k-mean and particle swarm optimization algorithm is proposed in the paper. K-mean clustering algorithm is a local search algorithm because it is easily to be trapped in local optimum and is sensitive to initial value effectively. On the other hand, particle swarm optimization algorithm is a global optimization algorithm. Because of taking the criterion function of k-mean as the object function of PSO, this algorithm incorporates the local search ability of k-mean algorithm with the global optimization ability of PSO. Experiments show that the new algorithm can get the optimal quantizated image by PSNR and RMSE.
Keywords :
image segmentation; particle swarm optimisation; pattern clustering; search problems; PSNR; PSO; RMSE; global optimization algorithm; hybrid algorithm; image segmentation; k-mean clustering algorithm; local search ability; local search algorithm; object function; particle swarm optimization algorithm; Algorithm design and analysis; Clustering algorithms; Image segmentation; Optimization; Particle swarm optimization; Partitioning algorithms; Vectors;
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-1743-6
DOI :
10.1109/ICACI.2012.6463233