Abstract
In the last several decades digital images were extend their usage in numerous areas. Due to various digital image processing methods they became part areas such as astronomy, agriculture and more. One of the main task in image processing application is segmentation. Since segmentation represents rather important problem, various methods were proposed in the past. One of the methods is to use clustering algorithms which is explored in this paper. We propose k-means algorithm for digital image segmentation. K-means algorithm's well known drawback is the high possibility of getting trapped into local optima. In this paper we proposed brain storm optimization algorithm for optimizing k-means algorithm used for digital image segmentation. Our proposed algorithm is tested on several benchmark images and the results are compared with other stat-of-the-art algorithms. The proposed method outperformed the existing methods.
| Original language | English |
|---|---|
| Title of host publication | 7th International Symposium on Digital Forensics and Security, ISDFS 2019 |
| Editors | Asaf Varol, Murat Karabatak, Cihan Varol, Sevginur Teke |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728128276 |
| DOIs | |
| Publication status | Published - 1 Jun 2019 |
| Event | 7th International Symposium on Digital Forensics and Security, ISDFS 2019 - Barcelos, Portugal Duration: 10 Jun 2019 → 12 Jun 2019 |
Publication series
| Name | 7th International Symposium on Digital Forensics and Security, ISDFS 2019 |
|---|
Conference
| Conference | 7th International Symposium on Digital Forensics and Security, ISDFS 2019 |
|---|---|
| Country | Portugal |
| City | Barcelos |
| Period | 10/6/19 → 12/6/19 |
Fingerprint
Keywords
- Clustering
- Digital image processing
- Machine learning
- Optimization
- Segmentation
- Swarm intelligence
ASJC Scopus subject areas
- Health Informatics
- Pathology and Forensic Medicine
- Computer Networks and Communications
- Computer Science Applications
- Safety, Risk, Reliability and Quality
Cite this
Clustering algorithm optimized by brain storm optimization for digital image segmentation. / Tuba, Eva; Jovanovic, Raka; Zivkovic, Dejan; Beko, Marko; Tuba, Milan.
7th International Symposium on Digital Forensics and Security, ISDFS 2019. ed. / Asaf Varol; Murat Karabatak; Cihan Varol; Sevginur Teke. Institute of Electrical and Electronics Engineers Inc., 2019. 8757552 (7th International Symposium on Digital Forensics and Security, ISDFS 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Clustering algorithm optimized by brain storm optimization for digital image segmentation
AU - Tuba, Eva
AU - Jovanovic, Raka
AU - Zivkovic, Dejan
AU - Beko, Marko
AU - Tuba, Milan
PY - 2019/6/1
Y1 - 2019/6/1
N2 - In the last several decades digital images were extend their usage in numerous areas. Due to various digital image processing methods they became part areas such as astronomy, agriculture and more. One of the main task in image processing application is segmentation. Since segmentation represents rather important problem, various methods were proposed in the past. One of the methods is to use clustering algorithms which is explored in this paper. We propose k-means algorithm for digital image segmentation. K-means algorithm's well known drawback is the high possibility of getting trapped into local optima. In this paper we proposed brain storm optimization algorithm for optimizing k-means algorithm used for digital image segmentation. Our proposed algorithm is tested on several benchmark images and the results are compared with other stat-of-the-art algorithms. The proposed method outperformed the existing methods.
AB - In the last several decades digital images were extend their usage in numerous areas. Due to various digital image processing methods they became part areas such as astronomy, agriculture and more. One of the main task in image processing application is segmentation. Since segmentation represents rather important problem, various methods were proposed in the past. One of the methods is to use clustering algorithms which is explored in this paper. We propose k-means algorithm for digital image segmentation. K-means algorithm's well known drawback is the high possibility of getting trapped into local optima. In this paper we proposed brain storm optimization algorithm for optimizing k-means algorithm used for digital image segmentation. Our proposed algorithm is tested on several benchmark images and the results are compared with other stat-of-the-art algorithms. The proposed method outperformed the existing methods.
KW - Clustering
KW - Digital image processing
KW - Machine learning
KW - Optimization
KW - Segmentation
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85070508296&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070508296&partnerID=8YFLogxK
U2 - 10.1109/ISDFS.2019.8757552
DO - 10.1109/ISDFS.2019.8757552
M3 - Conference contribution
AN - SCOPUS:85070508296
T3 - 7th International Symposium on Digital Forensics and Security, ISDFS 2019
BT - 7th International Symposium on Digital Forensics and Security, ISDFS 2019
A2 - Varol, Asaf
A2 - Karabatak, Murat
A2 - Varol, Cihan
A2 - Teke, Sevginur
PB - Institute of Electrical and Electronics Engineers Inc.
ER -