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Automated painter recognition based on image feature extraction

This paper describes an approach to automated classification of paintings by artist. The individual style of an artist is recognized through specific elements of a painting which distinguishes the work of an individual from the works of others. The proposed method for automated painter recognition focuses on the measurable elements in a painting which are represented with a set of global image features. The set of computed image descriptors includes statistical features that describe the intensity of a grayscale image, features based on color and textural features obtained using different techniques. Several classifiers were tested and their performance was evaluated on a collection of 500 digitized images of paintings from 20 different artists, obtained from various Internet sources. Experimental results show overall classification accuracy of 75%.