Paper Info
Title
Optimizing Fcm For Segmentation Of Image Using Gbest-Guided Artificial Bee Colony Algorithm
Abstract
Traditional fuzzy C-Means (FCM) clustering algorithm is sensitive to the initial value and converges to a local minimum easily that may lead to produce poor phenomena of segmenting image. This paper uses improved artificial bee colony to optimize FCM algorithm and uses improved methods to segment image. The improved algorithm (GABCFCM) uses Gbest-guided artificial bee colony (GABC) algorithm which searches the global best solution strongly, and it uses FCM which has ability to cluster data. Clustering segmentation of gray image is realized in this paper. Experiment results show that GABCFCM algorithm does not increase the consumption of time obviously and it improves the speed of converging to the global best solution by comparing with the ABCFCM which uses traditional artificial bee colony algorithm to optimize FCM. GABCFCM improved the reliability of image segmentation by using FCM algorithm greatly. GABCFCM can be applied to image processing of requiring higher relative velocity in the same number of cycles. This paper also uses some steps to improve PSABCFCM and PSABCLUTFCM and uses them in the fields of image segmentation.
Year
Venue
Keywords
2015
2015 11TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC)
Fuzzy C-Means clustering, Gbest-guide artificial bee colony algorithm, global best solution, image segmentation
Field
DocType
Citations 
Canopy clustering algorithm,Artificial bee colony algorithm,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Cluster analysis,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
6
3
Name
Order
Citations
PageRank
Xiping Song1193.54
Guoqin Li200.34
Lufeng Luo300.34