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Segmentation by Incremental Clustering
by
Dao Nam Anh
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 111 - Number 12 |
| Year of Publication: 2015 |
| Authors: Dao Nam Anh |
10.5120/19591-1360
|
Dao Nam Anh . Segmentation by Incremental Clustering. International Journal of Computer Applications. 111, 12 ( February 2015), 23-30. DOI=10.5120/19591-1360
@article{
10.5120/19591-1360,
author = {
Dao Nam Anh
},
title = { Segmentation by Incremental Clustering },
journal = {
International Journal of Computer Applications
},
issue_date = { February 2015 },
volume = { 111 },
number = { 12 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = {
23-30
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume111/number12/19591-1360/
},
doi = { 10.5120/19591-1360 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:47:42.923428+05:30
%A Dao Nam Anh
%T Segmentation by Incremental Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 12
%P 23-30
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract
A method for unsupervised segmentation by incremental clustering is introduced. Inspired by incremental approach and correlation clustering, clusters are added and refined during segmentation process. Correlation clustering is to keep away from pre-definition for number of clusters and incremental approach is to avoid re-clustering that is needed in iterative methods. The Gaussian spatial kernel is involved like a part of similarity function to cover local image structure. Cluster representative is updated efficiently to satisfy the old and new similarity constraints rather than re-clustering the entire image. Experimental results are discussed and show that the algorithm requires reasonable computational complexity while gaining a comparable or better segmentation quality than standard methods.
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Index Terms
Computer Science
Information Sciences
Keywords