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Pambudi, Elindra Ambar
- Image Segmentation Analysis Based on K-means PSO by Using Three Distance Measures
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Authors
Affiliations
1 Informatic Engineering, Universitas Muhammadiyah Purwokerto and Universitas Dian Nuswantoro, ID
2 Informatic Engineering, Universitas Dian Nuswantoro, ID
1 Informatic Engineering, Universitas Muhammadiyah Purwokerto and Universitas Dian Nuswantoro, ID
2 Informatic Engineering, Universitas Dian Nuswantoro, ID
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 1 (2018), Pagination: 1821-1826Abstract
The image segmentation is a technique of image processing which divides image into segments. The many proposed image segmentation techniques, k-Means clustering has been one of the basic image segmentation techniques. The advantages of k-Means are easy calculation, the number of small iteration, and one of the most commonly used clustering algorithm. but, The main problem in this algorithm is sensitive to selection initial cluster center. In this research, we present two approaches method which are used to execute image. It is PSO and k-Means. k-Means integrated with Particle Swarm Optimization (PSO) to improve the accuracy. The purpose of this research to find the effect of PSO towards k-Means in order to get the best selection initial cluster center. This research has been implemented using matlab and taking image dataset from weizzmann institute. The Result of our experiment, we have different result RMSE of k-Means PSO. Euclidean has less RMSE value than Manhattan. The difference RMSE between Euclidean PSO and Manhattan PSO only four point. but if we compare by processing time we have significant difference.Keywords
Vision Computing, Image Processing, Segmentation, Swarm Intelligence, Computer Science.
References
- J. Nikita Sharma, Mahendra Mishra and Manish Shrivastava, “Colour Image Segmentation Techniques and Issues: An Approach”, International Journal of Scientific and Technology, Vol. 1, No. 4, pp. 9-12, 2012.
- G. Komarasamy and Amitabh Wahi, “Improving the Cluster Performance by Combining PSO and K-Means Algorithm”, ICTACT Journal Soft Computing, Vol. 1, No. 4, pp. 206-208, 2011.
- Pritesh Vora and Bhavesh Oza, “A Suvey on K-Mean Clustering and Particle Swarm Optimization”, International Journal of Science and Modern Engineering, Vol. 1, No. 3, pp. 24-26, 2013.
- V.K. Panchal, H. Kundra and J. Kaur, “Comparative Study of Particle Swarm Optimization based Unsupervised Clustering Techniques”, International Journal of Computer Science and Network Security, Vol. 9, No. 10, pp. 132-140, 2009.
- Kahkashan Kouser and Sunita, “A Comparative Study of K Means Algorithm by Different Distance Measures”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, No. 9, pp. 24432447, 2013.
- Doreswamy and M. Umme Salma, “PSO Based Fast Kmeans Algorithm for Feature Selection from High
- Dimensional Medical Data Set”, Proceedings of 10th International Conference on Intelligent Systems and Control, pp. 1-6, 2016.
- Zeyad Safaa Younus, Dzulkifli Mohamad, Tanzila Saba, Mohammed Hasim Alkawaz, Amjad Rehman, Mznah AlRodhaan and Abdullah Al-Dhelaan, “Content-based Image Retrieval using PSO and K-Means Clustering Algorithm”, Arabian Journal of Geosciences, Vol. 8, No. 8, pp. 62116224, 2014.
- Xiaohui Cui and Thomas E. Potok, “Document Clustering Analysis Based on Hybrid PSO+K-Means Algorithm”, Available at:https://pdfs.semanticscholar.org/7c2a/8d6e5fe53248bccc49 b5fc605ddd7aa23021.pdf.
- J. Kennedy and R. Ebenhart, “A New Optimizer using Particle Swarm Theory”, Proceedings of IEEE 6th International Symposium on Micro Machine and Human Science, pp. 1-4, 2001.