Koi varieties identification based zero parameter simple linear iterative clustering and support vector machine

Setiawan, Alexander and Setyati, Endang and SAPPHIRA, AMADEA (2025) Koi varieties identification based zero parameter simple linear iterative clustering and support vector machine. [UNSPECIFIED]

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Abstract

There’s currently 120 types of koi fish that has been bred around the world. The types of koi fish depend on the colour patterns and shapes they have. There’s alot of patterns that has similarity between one type with another. For example, sanke and showa koi fish will look similar from a non-expert’s point of view, because both type has same colour pattern, which is red, black and white. In actuality, sanke koi is dominantly red and white with slight black accent, while showa’s dominant colour is red and black, with white accent. In this research, Zero Parameter Simple Linear Iterative Clustering (SLICO) method and Simple Linear Iterative Clustering (SLIC) will be tested and used to process the image segmentation process to eliminate the background of the image. Colour Local Binary Pattern method is used to get the textures on images through the RGB, HSV, and grayscale colour space. Support Vector Machine is used to identify types of koi fish. To test the SVM, two kind of kernel is used, which is linear kernel and Radial Basis Function (RBF) kernel. The results of this study are the program able to recognize types of koi from images. The test results show an accuracy of 36% in grayscale colour space, 50% in RGB colour space, and 48% in HSV colour space.

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