ICTACT Journal on Image and Video Processing

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Image Segmentation Analysis Based on K-means PSO by Using Three Distance Measures


Affiliations
1 Informatic Engineering, Universitas Muhammadiyah Purwokerto and Universitas Dian Nuswantoro, Indonesia
2 Informatic Engineering, Universitas Dian Nuswantoro, Indonesia
     

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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.
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  • Image Segmentation Analysis Based on K-means PSO by Using Three Distance Measures

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Authors

Elindra Ambar Pambudi
Informatic Engineering, Universitas Muhammadiyah Purwokerto and Universitas Dian Nuswantoro, Indonesia
Pulung Nurtantio Andono
Informatic Engineering, Universitas Dian Nuswantoro, Indonesia
Ricardus Anggi Pramunendar
Informatic Engineering, Universitas Dian Nuswantoro, Indonesia

Abstract


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