Impressionism, expressionism, surrealism

TLDR

The study proposes an automated method to recognize painters and art schools by analyzing their signature styles and computer-based visual perception. The approach analyzes nine artists from three schools using extensive image features and transforms, selects the most informative descriptors via Fisher scores, and employs them for classification and similarity of paintings, painters, and schools. The method achieves 77 % accuracy in classifying individual painters and 91 % in identifying their art school, can automatically group artists sharing a school unsupervised, and its source code is freely available.

Abstract

We describe a method for automated recognition of painters and schools of art based on their signature styles and studied the computer-based perception of visual art. Paintings of nine artists, representing three different schools of art—impressionism, surrealism and abstract expressionism—were analyzed using a large set of image features and image transforms. The computed image descriptors were assessed using Fisher scores, and the most informative features were used for the classification and similarity measurements of paintings, painters, and schools of art. Experimental results show that the classification accuracy when classifying paintings into nine painter classes is 77%, and the accuracy of associating a given painting with its school of art is 91%. An interesting feature of the proposed method is its ability to automatically associate different artists that share the same school of art in an unsupervised fashion. The source code used for the image classification and image similarity described in this article is available for free download.