Color Identification Uses the K-Means Clustering Method

Karma, I Gede Made and Putra, I Ketut Gede Darma and Sudarma, Made and Linawati, Linawati (2018) Color Identification Uses the K-Means Clustering Method. In: International Conference on Applied Science and Technology (iCAST) 2018, October 26-27, 2018, Manado, Indonesia.

	Text (Artikel) iCAST-Karma-Color Identification uses the K-Means Clustering Method.pdf - Accepted Version Download (1MB)
	Text (Acceptance Email) Politeknik Negeri Bali Mail - [iCAST 2018] Congratulation Your paper #1570493814 ('Color Identification Uses the K-Means Clustering Method') - Accepted.pdf - Other Download (115kB)
	Text (Parallel Session) iCAST 2018 - Parallel Session.pdf - Other Download (267kB)
	Text (TOC of Proceeding) Table_of_contents-iCAST 2018.pdf - Other Download (111kB)

Abstract

The segmentation process plays a very important role in the process of detecting and recognizing objects in an image. Although many segmentation methods have been developed, there is no method that can give good results and is generally accepted. This study aims to find a better segmentation method in finding patterns or ground-truths of objects in an image. The procedure applied is to compare the segmentation results from the proposed color dissimilarity based segmentation method with several well-known and widely used methods. Based on the results shown, several methods were not successful in showing the pattern of objects contained in the sample image. From the segmentation method being compared, it turns out that the proposed color dissimilarity based segmentation method shows the best results, making it suitable for application in the process of object detection and recognition.

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