Modified K-means combined with artificial bee colony algorithm and differential evolution for color image segmentation
Clustering is one of most commonly used approach in the literature of Pattern recognition and Machine Learning. K-means clustering algorithm is a fast and simple method in the clustering approaches. However, due to random selection of center of clusters and the adherence to preliminary results of ce...
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| Format: | Conference or Workshop Item |
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2015
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| _version_ | 1796860699315535872 |
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| author | Bonab, Mohammad Babrdel Mohd. Hashim, Siti Zaiton Alsaedi, Ahmed Khalaf Zager Hashim, Ummi Raba'ah |
| author_facet | Bonab, Mohammad Babrdel Mohd. Hashim, Siti Zaiton Alsaedi, Ahmed Khalaf Zager Hashim, Ummi Raba'ah |
| author_sort | Bonab, Mohammad Babrdel |
| collection | ePrints |
| description | Clustering is one of most commonly used approach in the literature of Pattern recognition and Machine Learning. K-means clustering algorithm is a fast and simple method in the clustering approaches. However, due to random selection of center of clusters and the adherence to preliminary results of center of clusters, the risk of trapping to a local minimum ever exist.in this study, we have taken help of effective hybrid of optimization algorithms, artificial bee colony (ABC) and differential evolution (DE), is proposed as a method to mentioned problems. The proposed method consists of two main steps. In first step, Seed Cluster Center Algorithm employed to best initial cluster centers. The combined evolutionary algorithm explores the solution space to find global solution. The performance of proposed method evaluated with standard data set. The evaluation results of the proposed algorithm and its comparison with other alternative algorithms in literature confirms its superior performance and higher efficiency. |
| first_indexed | 2024-03-05T19:45:12Z |
| format | Conference or Workshop Item |
| id | utm.eprints-59380 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:45:12Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | utm.eprints-593802021-12-14T03:27:07Z http://eprints.utm.my/59380/ Modified K-means combined with artificial bee colony algorithm and differential evolution for color image segmentation Bonab, Mohammad Babrdel Mohd. Hashim, Siti Zaiton Alsaedi, Ahmed Khalaf Zager Hashim, Ummi Raba'ah QA75 Electronic computers. Computer science Clustering is one of most commonly used approach in the literature of Pattern recognition and Machine Learning. K-means clustering algorithm is a fast and simple method in the clustering approaches. However, due to random selection of center of clusters and the adherence to preliminary results of center of clusters, the risk of trapping to a local minimum ever exist.in this study, we have taken help of effective hybrid of optimization algorithms, artificial bee colony (ABC) and differential evolution (DE), is proposed as a method to mentioned problems. The proposed method consists of two main steps. In first step, Seed Cluster Center Algorithm employed to best initial cluster centers. The combined evolutionary algorithm explores the solution space to find global solution. The performance of proposed method evaluated with standard data set. The evaluation results of the proposed algorithm and its comparison with other alternative algorithms in literature confirms its superior performance and higher efficiency. 2015 Conference or Workshop Item PeerReviewed Bonab, Mohammad Babrdel and Mohd. Hashim, Siti Zaiton and Alsaedi, Ahmed Khalaf Zager and Hashim, Ummi Raba'ah (2015) Modified K-means combined with artificial bee colony algorithm and differential evolution for color image segmentation. In: 4th International Neural Network Society Symposia Series on Computational Intelligence in Information Systems, INNS-CIIS 2014, 7 - 9 November 2014, Bandar Seri Begawan, Brunei. http://dx.doi.org/10.1007/978-3-319-13153-5_22 |
| spellingShingle | QA75 Electronic computers. Computer science Bonab, Mohammad Babrdel Mohd. Hashim, Siti Zaiton Alsaedi, Ahmed Khalaf Zager Hashim, Ummi Raba'ah Modified K-means combined with artificial bee colony algorithm and differential evolution for color image segmentation |
| title | Modified K-means combined with artificial bee colony algorithm and differential evolution for color image segmentation |
| title_full | Modified K-means combined with artificial bee colony algorithm and differential evolution for color image segmentation |
| title_fullStr | Modified K-means combined with artificial bee colony algorithm and differential evolution for color image segmentation |
| title_full_unstemmed | Modified K-means combined with artificial bee colony algorithm and differential evolution for color image segmentation |
| title_short | Modified K-means combined with artificial bee colony algorithm and differential evolution for color image segmentation |
| title_sort | modified k means combined with artificial bee colony algorithm and differential evolution for color image segmentation |
| topic | QA75 Electronic computers. Computer science |
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