Impressionism, expressionism, surrealism: Automated recognition of painters and schools of art

Authors:
Lior Shamir;Tomasz Macura;Nikita Orlov;D. Mark Eckley;Ilya G. Goldberg
Affiliations:
Image Informatics Group/Laboratory of Genetics/NIA/NIH, Baltimore, MD;Image Informatics Group/Laboratory of Genetics/NIA/NIH, Baltimore, MD;Image Informatics Group/Laboratory of Genetics/NIA/NIH, Baltimore, MD;Image Informatics Group/Laboratory of Genetics/NIA/NIH, Baltimore, MD;Image Informatics Group/Laboratory of Genetics/NIA/NIH, Baltimore, MD
Venue:
ACM Transactions on Applied Perception (TAP)
Year:
2010

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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.