FAST IMAGE SEGMENTATION USING C-MEANS BASED
Author:
, , , , ,Year
: 2008
Abstract: In this paper, we propose a fast C-means based training of
Fuzzy Hopfield neural network and apply it to image
segmentation. According to the other ways which usually
take a long time, we define a fast method for image
segmentation. We present a new objective function, and its
minimization by Lyapunov energy function which is based
on two dimensional fuzzy Hopfield neural network. This
objective function is the same energy function Hopfield
neural network which is improved, and includes average
distance between image pixels and cluster centers. In this
new method, numbers of iterations are less than the other
methods it means the proposed method has a faster
convergence rate in comparison with the other ways.
Therefore, Fuzzy Hopfield neural network method provides
image segmentation better than the other methods according
to experimental results.
Fuzzy Hopfield neural network and apply it to image
segmentation. According to the other ways which usually
take a long time, we define a fast method for image
segmentation. We present a new objective function, and its
minimization by Lyapunov energy function which is based
on two dimensional fuzzy Hopfield neural network. This
objective function is the same energy function Hopfield
neural network which is improved, and includes average
distance between image pixels and cluster centers. In this
new method, numbers of iterations are less than the other
methods it means the proposed method has a faster
convergence rate in comparison with the other ways.
Therefore, Fuzzy Hopfield neural network method provides
image segmentation better than the other methods according
to experimental results.
Keyword(s): segmentation,fuzzy,neural network,
Hopfield neural network
Collections
:
-
Statistics
FAST IMAGE SEGMENTATION USING C-MEANS BASED
Show full item record
| contributor author | فرهاد محمد کاظمی | en |
| contributor author | محمدرضا اکبرزاده توتونچی | en |
| contributor author | سعید راحتی | en |
| contributor author | حبیب رجبی مشهدی | en |
| contributor author | Mohammad Reza Akbarzadeh Totonchi | fa |
| contributor author | Habib Rajabi Mashhadi | fa |
| date accessioned | 2020-06-06T13:51:36Z | |
| date available | 2020-06-06T13:51:36Z | |
| date copyright | 5/4/2008 | |
| date issued | 2008 | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/3372209?locale-attribute=en | |
| description abstract | In this paper, we propose a fast C-means based training of Fuzzy Hopfield neural network and apply it to image segmentation. According to the other ways which usually take a long time, we define a fast method for image segmentation. We present a new objective function, and its minimization by Lyapunov energy function which is based on two dimensional fuzzy Hopfield neural network. This objective function is the same energy function Hopfield neural network which is improved, and includes average distance between image pixels and cluster centers. In this new method, numbers of iterations are less than the other methods it means the proposed method has a faster convergence rate in comparison with the other ways. Therefore, Fuzzy Hopfield neural network method provides image segmentation better than the other methods according to experimental results. | en |
| language | English | |
| title | FAST IMAGE SEGMENTATION USING C-MEANS BASED | en |
| type | Conference Paper | |
| contenttype | External Fulltext | |
| subject keywords | segmentation | en |
| subject keywords | fuzzy | en |
| subject keywords | neural network | en |
| subject keywords | Hopfield neural network | en |
| identifier link | https://profdoc.um.ac.ir/paper-abstract-1007717.html | |
| conference title | Canadian Conference on Electrical and Computer Engineering | en |
| identifier articleid | 1007717 |