Research of color image segmentation algorithm based on asymmetric kernel density estimation

2017 ◽  
Vol 17 (3) ◽  
pp. 455-462 ◽  
Author(s):  
Feng-Xian Tang ◽  
Yun-Feng Yang
Keyword(s):  
Image Segmentation ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Color Image ◽  
Kernel Density ◽  
Segmentation Algorithm ◽  
Color Image Segmentation ◽  
Asymmetric Kernel ◽  
Image Segmentation Algorithm

Color Image Segmentation Based on Self-Organizing Maps

2011 ◽  
Vol 214 ◽  
pp. 693-698
Author(s):  
Rui Geng
Keyword(s):  
Image Segmentation ◽  
Optimization Problems ◽  
Color Image ◽  
Optimal Solution ◽  
Implementation Process ◽  
Segmentation Algorithm ◽  
Color Image Segmentation ◽  
Combinatorial Optimization Problems ◽  
Bee Colony ◽  
Image Segmentation Algorithm

The colony intellectual behavior performed by many organisms in nature can solve various kinds of problems on scientific and technological research. Bees are a socialized insect colony, which perform different types of activities according to their different divisions of labor, and achieve information sharing and exchanges among the bee colony to find the optimal solution for problems. According to this characteristic, researchers have proposed the algorithm of bee colony for solving combinatorial optimization problems. In this paper, it will describe the implementation process of such an image segmentation algorithm, and the result shows that this method is a potential image segmentation algorithm.

Fuzzy Clustering Color Image Segmentation Algorithm Based on CPSO

2012 ◽  
Vol 461 ◽  
pp. 526-531
Author(s):  
Xiao Hong Zhang ◽  
Hong Mei Ning
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Segmentation Algorithm ◽  
Color Image Segmentation ◽  
Local Optimum ◽  
Fcm Clustering ◽  
Clustering Center ◽  
Membership Matrix ◽  
Image Segmentation Algorithm

Fuzzy C-mean algorithm (FCM) has been well used in the field of color image segmentation. But it is sensitive to initial clustering center and membership matrix, and likely converges into the local minimum, which causes the quality of image segmentation lower. By use of the properties-ergodicity, randomicity of chaos, a new image segmentation algorithm is proposed, which combines the chaos particle swarm optimization (CPSO) and FCM clustering. Some experimental results are shown that this method not only has the ability to prevent the particles to convergence to local optimum, but also has faster convergence and higher accuracy for segmentation. Using the feature distance instead of Euclidian distance, robustness of this method is enhanced.

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