Classification of Pointillist paintings using colour and texture features

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.