Genre and Style Based Painting Classification

Agarwal, Sonali; Karnick, Harish; Pant, Nirmal; Patel, Urvesh D.

doi:10.1109/wacv.2015.84

Cited by 43 publications

(37 citation statements)

References 18 publications

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“…Indeed, there are other approaches that are more accurate. For instance, Zujovic et al (65) achieved accuracies of ∼70% in a classification task with 353 paintings from five styles, and Argarwal et al (66) reported an accuracy of ∼60% in a classification task with 3,000 paintings from 10 styles. However, our results cannot be directly compared with those works, since they use a much smaller dataset with fewer styles and several image features, while our predictions are based only on two features.…”

Section: Predicting Artistic Stylesmentioning

confidence: 99%

History of art paintings through the lens of entropy and complexity

Sigaki

Perc

Ribeiro

2018

Proc. Natl. Acad. Sci. U.S.A.

107

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Art is the ultimate expression of human creativity that is deeply influenced by the philosophy and culture of the corresponding historical epoch. The quantitative analysis of art is therefore essential for better understanding human cultural evolution. Here, we present a large-scale quantitative analysis of almost 140,000 paintings, spanning nearly a millennium of art history. Based on the local spatial patterns in the images of these paintings, we estimate the permutation entropy and the statistical complexity of each painting. These measures map the degree of visual order of artworks into a scale of order-disorder and simplicity-complexity that locally reflects qualitative categories proposed by art historians. The dynamical behavior of these measures reveals a clear temporal evolution of art, marked by transitions that agree with the main historical periods of art. Our research shows that different artistic styles have a distinct average degree of entropy and complexity, thus allowing a hierarchical organization and clustering of styles according to these metrics. We have further verified that the identified groups correspond well with the textual content used to qualitatively describe the styles and the applied complexity-entropy measures can be used for an effective classification of artworks.

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Section: Predicting Artistic Stylesmentioning

confidence: 99%

History of art paintings through the lens of entropy and complexity

Sigaki

Perc

Ribeiro

2018

Proc. Natl. Acad. Sci. U.S.A.

107

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“…• Artistic genre dataset [2] contains images collected from WikiArt and grouped into: Abstract-expressionism, Baroque, Cubism, Impressionism, Expressionism, Pop Art, Rococo, Realism, Renaissance and Surrealism.…”

Section: Methodsmentioning

confidence: 99%

“…• Histogram of oriented gradients (HOG) [9] which computes the oriented gradient in each pixel and accumulates the weight of each orientation into a histogram. It has been previously used in painting analysis [13], [2].…”

Section: Features and Classifiersmentioning

confidence: 99%

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Pandora: Description of a painting database for art movement recognition with baselines and perspectives

Florea

Condorovici

Vertan

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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To facilitate computer analysis of visual art, in the form of paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art movement) database, a collection of digitized paintings labelled with respect to the artistic movement. Noting that the set of databases available as benchmarks for evaluation is highly reduced and most existing ones are limited in variability and number of images, we propose a novel large scale dataset of digital paintings. The database consists of more than 7700 images from 12 art movements. Each genre is illustrated by a number of images varying from 250 to nearly 1000. We investigate how local and global features and classification systems are able to recognize the art movement. Our experimental results suggest that accurate recognition is achievable by a combination of various categories.

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“…A summary of various directions approached, algorithms and results for not-so-recent solutions can be found in the review of Bentowska and Coddington 2010. However, a plurality of works (Agarwal et al, 2015;Karayev et al, 2014;Bar et al, 2014;Florea et al, 2017b) addressed style (art movement) recognition as it is the main label associated with paintings. An intermediate topic is in the work of Monroy et al 2014 which detected and extracted shapes (associated with objects) but as pre-processing for the final task which was that of restoration.…”

Section: Related Workmentioning

confidence: 99%

Can we teach computers to understand art? Domain adaptation for enhancing deep networks capacity to de-abstract art

Badea

Florea

et al. 2018

Image and Vision Computing

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Humans comprehend a natural scene at a single glance; painters and other visual artists, through their abstract representations, stressed this capacity to the limit. The performance of computer vision solutions matched that of humans in many problems of visual recognition. In this paper we address the problem of recognizing the genre (subject) in digitized paintings using Convolutional Neural Networks (CNN) as part of the more general dealing with abstract and/or artistic representation of scenes. Initially we establish the state of the art performance by training a CNN from scratch. In the next level of evaluation, we identify aspects that hinder the CNNs' recognition, such as artistic abstraction. Further, we test various domain adaptation methods that could enhance the subject recognition capabilities of the CNNs. The evaluation is performed on a database of 80,000 annotated digitized paintings, which is tentatively extended with artistic photographs, either original or stylized, in order to emulate artistic representations. Surprisingly, the most efficient domain adaptation is not the neural style transfer. Finally, the paper provides an experiment-based assessment of the abstraction level that CNNs are able to achieve.

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Genre and Style Based Painting Classification

Cited by 43 publications

References 18 publications

History of art paintings through the lens of entropy and complexity

History of art paintings through the lens of entropy and complexity

Pandora: Description of a painting database for art movement recognition with baselines and perspectives

Can we teach computers to understand art? Domain adaptation for enhancing deep networks capacity to de-abstract art

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