Abstract
Fine art paintings classification based on artistic style is a field of growing interest. Pointillist style is one of the most easily recognized painting styles by humans, due to its characteristic tiny detached paintbrushes of pure colour. In this paper automatic discrimination of artworks belonging to the style of Pointillism is investigated. The opposite styles considered are Cubism, Purism, Naïve art and Impressionism. Several colour and texture features are considered and a feature selection procedure is employed to reveal the most relevant ones to pointillist movement. Binary classification is performed, both in supervised and unsupervised mode, to assess the features’ discriminative ability. A small number of selected features is shown, by simulations results, to be quite powerful predictors resulting in a classification accuracy of 94% for a SVM classifier, 93.5% for a KNN classifier and 87% for a k-means classifier.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Electrical and Computer Engineering Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
