Classifying paintings by artistic genre: An analysis of features &amp;#x00026; classifiers

Zujovic, Jana; Gandy, Lisa; Friedman, Sara L.; Pardo, Bryan; Pappas, Thrasyvoulos N.

doi:10.1109/mmsp.2009.5293271

Cited by 81 publications

(46 citation statements)

References 15 publications

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“…Zujovic et al [13] proposed the classification method for five genres: abstract expressionism, cubism, impressionism, pop art, and realism. Features of paintings are extracted using the edge map as the gray-level feature and histogram as color feature in HSV space.…”

Section: Related Workmentioning

confidence: 99%

Style classification and visualization of art painting’s genre using self-organizing maps

Lee

Cha

2016

Hum. Cent. Comput. Inf. Sci.

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With the spread of digitalization of art paintings, research on diverse scientific approaches on painted images has become active. In this paper, the method of classifying painting styles by extracting various features from paintings is suggested. Global features are extracted using the color-based statistical computation and composition-based local features of paintings are extracted through the segmentation of objects within the paintings to classify the styles of the paintings. Based on the extracted features, paintings are categorized by style using SOM, which are then analyzed through visualization using the map. We have proved the feasibility of the proposed method of categorizing paintings by style, and the objective features of paintings can contribute to the research on art history and aesthetics.

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Section: Related Workmentioning

confidence: 99%

Style classification and visualization of art painting’s genre using self-organizing maps

Lee

Cha

2016

Hum. Cent. Comput. Inf. Sci.

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“…The main difficulty in comparing the proposed approach with state os the art solutions [5], [4], [8] is the use of different databases. We stress that our database is ≈ 10 times larger than any reported solution.…”

Section: Comparison With State Of the Artmentioning

confidence: 99%

“…Systems with low-level features, were proposed by Gunsel et al [4] that discriminates among three genres using six basic features extracted only from the luminance image and by Zujovic et al [5] relied on a set of gray-level features for a five genre classification. The downside of these methods is the low number of paintings used to test the systems (107 for [4] and 353 for [5]).…”

Section: Introductionmentioning

confidence: 99%

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Perceptually-Inspired Artistic Genre Identification System in Digitized Painting Collections

et al. 2013

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Abstract. This paper presents an automatic system for the recognition of artistic genre in digital representations of paintings. This solution comes as part of the recent extensive effort of developing image processing solutions that facilitate a better understanding of art. As art addresses human perception, the current extracted features are perceptually inspired. While 3D Color Histogram and Gabor Filter Energy have been used for art description, frameworks extracted using anchoring theory are novel in this field. The paper investigates the possible use of 7 classifiers and the resulting performance, as evaluated on a database containing more than 3400 paintings from 6 different genres, outperforms the reported state of the art.

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“…Attempts have focused mostly on supervised classification of paintings by their creating artists [Shen 2009;Zujovic et al 2009], or associating paintings with captions and key words [Kammer et al 2007; Barnard et al 2001]. Some studies also applied automatic methods to determine the methods of drawing [Roussopoulos et al 2010].…”

Section: Introductionmentioning

confidence: 99%

Computer analysis of art

Shamir

Tarakhovsky

2012

J. Comput. Cult. Herit.

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Analysis of visual art is a highly complex cognitive task that depends on the very many aspects of the art as well as complex brain connectivity, and the examination of visual art and the analysis of influential links between artists and artistic movements require the trained eye of knowledgeable art historians. However, while the human eye and brain can perceive visual art and notice the differences, similarities, and influential links between painters, computers employing artificial intelligence find this task far more challenging. In this article we show that computers can automatically analyze paintings of different artists and different schools of art in an unsupervised fashion. Experimental results show that the automatic computer analysis can group artists by their artistic movements, and provide a map of similarities and influential links that is largely in agreement with the analysis of art historians. These results demonstrate that machine vision and pattern recognition algorithms are able to mimic the complex cognitive task of the human perception of visual art, and can be used to measure and quantify visual similarities between paintings, painters, and schools of art.

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Classifying paintings by artistic genre: An analysis of features & classifiers

Cited by 81 publications

References 15 publications

Style classification and visualization of art painting’s genre using self-organizing maps

Style classification and visualization of art painting’s genre using self-organizing maps

Perceptually-Inspired Artistic Genre Identification System in Digitized Painting Collections

Computer analysis of art

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