Classification of artistic styles using binarized features derived from a deep neural network

Yaniv Bar; Noga Levy; Lior Wolf

doi:10.1007/978-3-319-16178-5_5

Classification of artistic styles using binarized features derived from a deep neural network

Yaniv Bar^*, Noga Levy, Lior Wolf

^*Corresponding author for this work

School of Computer Science

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

37 Scopus citations

Abstract

With the vast expansion of digital contemporary painting collections, automatic theme stylization has grown in demand in both academic and commercial fields. The recent interest in deep neural networks has provided powerful visual features that achieve state-of-the-art results in various visual classification tasks. In this work, we examine the perceptiveness of these features in identifying artistic styles in paintings, and suggest a compact binary representation of the paintings. Combined with the PiCoDes descriptors, these features show excellent classification results on a large scale collection of paintings.

Original language	English
Title of host publication	Computer Vision - ECCV 2014 Workshops, Proceedings
Editors	Michael M. Bronstein, Carsten Rother, Lourdes Agapito
Publisher	Springer Verlag
Pages	71-84
Number of pages	14
ISBN (Electronic)	9783319161778
DOIs	https://doi.org/10.1007/978-3-319-16178-5_5
State	Published - 2015
Event	13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland Duration: 6 Sep 2014 → 12 Sep 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8925
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th European Conference on Computer Vision, ECCV 2014
Country/Territory	Switzerland
City	Zurich
Period	6/09/14 → 12/09/14

Access to Document

10.1007/978-3-319-16178-5_5

Cite this

Bar, Y., Levy, N., & Wolf, L. (2015). Classification of artistic styles using binarized features derived from a deep neural network. In M. M. Bronstein, C. Rother, & L. Agapito (Eds.), Computer Vision - ECCV 2014 Workshops, Proceedings (pp. 71-84). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8925). Springer Verlag. https://doi.org/10.1007/978-3-319-16178-5_5

Bar, Yaniv ; Levy, Noga ; Wolf, Lior. / Classification of artistic styles using binarized features derived from a deep neural network. Computer Vision - ECCV 2014 Workshops, Proceedings. editor / Michael M. Bronstein ; Carsten Rother ; Lourdes Agapito. Springer Verlag, 2015. pp. 71-84 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Classification of artistic styles using binarized features derived from a deep neural network",

abstract = "With the vast expansion of digital contemporary painting collections, automatic theme stylization has grown in demand in both academic and commercial fields. The recent interest in deep neural networks has provided powerful visual features that achieve state-of-the-art results in various visual classification tasks. In this work, we examine the perceptiveness of these features in identifying artistic styles in paintings, and suggest a compact binary representation of the paintings. Combined with the PiCoDes descriptors, these features show excellent classification results on a large scale collection of paintings.",

author = "Yaniv Bar and Noga Levy and Lior Wolf",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",

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Bar, Y, Levy, N & Wolf, L 2015, Classification of artistic styles using binarized features derived from a deep neural network. in MM Bronstein, C Rother & L Agapito (eds), Computer Vision - ECCV 2014 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8925, Springer Verlag, pp. 71-84, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 6/09/14. https://doi.org/10.1007/978-3-319-16178-5_5

Classification of artistic styles using binarized features derived from a deep neural network. / Bar, Yaniv; Levy, Noga; Wolf, Lior.
Computer Vision - ECCV 2014 Workshops, Proceedings. ed. / Michael M. Bronstein; Carsten Rother; Lourdes Agapito. Springer Verlag, 2015. p. 71-84 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8925).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - With the vast expansion of digital contemporary painting collections, automatic theme stylization has grown in demand in both academic and commercial fields. The recent interest in deep neural networks has provided powerful visual features that achieve state-of-the-art results in various visual classification tasks. In this work, we examine the perceptiveness of these features in identifying artistic styles in paintings, and suggest a compact binary representation of the paintings. Combined with the PiCoDes descriptors, these features show excellent classification results on a large scale collection of paintings.

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Bar Y, Levy N, Wolf L. Classification of artistic styles using binarized features derived from a deep neural network. In Bronstein MM, Rother C, Agapito L, editors, Computer Vision - ECCV 2014 Workshops, Proceedings. Springer Verlag. 2015. p. 71-84. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-16178-5_5

Classification of artistic styles using binarized features derived from a deep neural network

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