Title :
Image Segmentation Using Fast Fuzzy C Means Based on Particle Swarm Optimization
Author :
Zang Jing ; Li Bo
Author_Institution :
Info. Sci. & Eng. Coll., Shenyang Ligong Univ., Shenyang, China
Abstract :
Present a image segmentation technique using fast fuzzy C Means clustering algorithm based on Particle Swarm Optimization Algorithm. This Algorithm utilizes the strong ability of the global optimizing of the PSO Algorithm, and avoids the sensitivity to local optimization of the Fast FCM algorithm. Furthermore, the PSO defines the centers and numbers of clustering automatically. Two algorithm combined to find a global optimizing clustering. Finally, Applies the crops diseases image, cuts apart the focus from the original image, the experimental result reveals it eliminates FCM trapped local optimum and being sensitive to initial value effectively, it is easily compute, better segmentation and better robustness.
Keywords :
crops; diseases; fuzzy set theory; image segmentation; particle swarm optimisation; pattern clustering; FCM trapped local optimum; PSO algorithm; crop disease image; fast FCM algorithm; fast fuzzy C means clustering algorithm; image segmentation; particle swarm optimization; Particle Swarm Optimization; fast fuzzy C means(FCM); image segmentation;
Conference_Titel :
Intelligent Networks and Intelligent Systems (ICINIS), 2010 3rd International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-8548-2
Electronic_ISBN :
978-0-7695-4249-2
DOI :
10.1109/ICINIS.2010.36