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Image classification of artworks based on multiple naive Bayes algorithm

Image classification of artworks based on multiple naive Bayes algorithm In order to overcome the problems of unreasonable contrast and low classification accuracy in traditional art image classification, this paper proposes a new art image classification method based on multiple naive Bayes algorithm. This method uses image smoothing method to remove the noise of the artwork images and realise image preprocessing. It constructs colour histogram to quantify the hue value of the artwork images to complete image feature extraction. The method obtains image feature points through multiple naive Bayes, then obtains the generated feature descriptor of the artwork images and finally, realises the classification of the artwork images. The experimental results show that the classification accuracy of this method is as high as 99.6%, the image classification contrast is always in the best value, and the classification time is short. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Arts and Technology Inderscience Publishers

Image classification of artworks based on multiple naive Bayes algorithm

International Journal of Arts and Technology , Volume 13 (2): 12 – Jan 1, 2021

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1754-8853
eISSN
1754-8861
DOI
10.1504/IJART.2021.120580
Publisher site
See Article on Publisher Site

Abstract

In order to overcome the problems of unreasonable contrast and low classification accuracy in traditional art image classification, this paper proposes a new art image classification method based on multiple naive Bayes algorithm. This method uses image smoothing method to remove the noise of the artwork images and realise image preprocessing. It constructs colour histogram to quantify the hue value of the artwork images to complete image feature extraction. The method obtains image feature points through multiple naive Bayes, then obtains the generated feature descriptor of the artwork images and finally, realises the classification of the artwork images. The experimental results show that the classification accuracy of this method is as high as 99.6%, the image classification contrast is always in the best value, and the classification time is short.

Journal

International Journal of Arts and TechnologyInderscience Publishers

Published: Jan 1, 2021

There are no references for this article.