Open AccessColour recognition using colour histogram feature extraction and K-nearest neighbour classifier
Author(s) -
Rabia Bayraktar,
Batur Alp Akgül,
Kadir Sercan Bayram
Publication year - 2020
Publication title -
new trends and issues proceedings on advances in pure and applied sciences
Language(s) - English
Resource type - Journals
ISSN - 2547-880X
DOI - 10.18844/gjpaas.v0i12.4981
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , histogram , feature extraction , artificial neural network , classifier (uml) , histogram of oriented gradients , computer vision , image processing , image (mathematics)
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.