Colour recognition using colour histogram feature extraction and K-nearest neighbour classifier
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Abstract
K-nearest neighbours (KNN) is a widely used neural network and machine learning classification algorithm. Recently, it has been used in the neural network and digital image processing fields. In this study, the KNN classifier is used to distinguish 12 different colours. These colours are black, blue, brown, forest green, green, navy, orange, pink, red, violet, white and yellow. Using colour histogram feature extraction, which is one of the image processing techniques, the features that distinguish these colours are determined. These features increase the effectiveness of the KNN classifier. The training data consist of saved frames and the test data are obtained from the video camera in real-time. The video consists of consecutive frames. The frames are 100 × 70 in size. Each frame is tested with K = 3,5,7,9 and the obtained results are recorded. In general, the best results are obtained when used K = 5.
Keywords: KNN algorithm, classifier, application, neural network, image processing, developed, colour, dataset, colour recognition.
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