Kmeans Clustering Segmentation on Water Microbial Image with Color and Texture Feature Extraction
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Sepyan Purnama Kristanto
*
Politeknik Negeri Banyuwangi, Banyuwangi,
Indonesia
- Lutfi Hakim Politeknik Negeri Banyuwangi, Banyuwangi, Indonesia
- Dianni Yusuf Politeknik Negeri Banyuwangi, Banyuwangi, Indonesia (*) Corresponding Author
Abstract
Image segmentation is one of the analytical processes for digital image recognition, where this process divides the digital image into several unique regions based on homogeneous pixels. The process of homogeneous grouping images is based on several colour, texture and shape features. Colour in digital image processing is very important because colour has many information humans can easily understand. Colour has various features, combining colour intensity and grey (grayscale) and binary (black and white) values. However, the colour feature extraction process has many weaknesses. If the object used has a very small si[1]ze and range area, the use of colour features needs to be combined with extraction, and the segmentation process can be maximized. This study uses colour and texture features in the extraction process. It uses bacterial objects (microbes) from water, with limited image quality and objects that tend to be difficult to identify. The colour space feature extraction process is combined with a Gabor filter so that the segmentation process produces high-quality accuracy. Good. The Gabor filter used in this study is combined with the L*a*b space vector to increase accuracy in the segmentation process. The results showed that the use of texture features resulted in an increase in accuracy of 17.5% by testing the cluster value of 1.2.
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