| Title | Clustering Algorithm Optimized by Brain Storm Optimization for Digital Image Segmentation |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Tuba, Eva, Jovanovic, Raka, Zivkovic, Dejan, Beko, Marko, Tuba, Milan |
| Conference Name | 2019 7th International Symposium on Digital Forensics and Security (ISDFS) |
| Date Published | jun |
| Keywords | benchmark images, brain storm optimization algorithm, clustering, Clustering algorithms, composability, compositionality, digital image processing, digital image processing methods, digital image segmentation, evolutionary computation, image processing application, image segmentation, k-means algorithm, learning (artificial intelligence), machine learning, Optimization, pubcrawl, Segmentation, swarm intelligence |
| 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. |
| DOI | 10.1109/ISDFS.2019.8757552 |
| Citation Key | tuba_clustering_2019 |
Clustering Algorithm Optimized by Brain Storm Optimization for Digital Image Segmentation