Recent advances in imaging and multimedia technologies have paved the way for automatic analysis of visual art. Despite notable attempts, extracting relevant patterns from paintings is still a challenging task. Different painters, born in different periods and places, have been influenced by different schools of arts. However, each individual artist also has a unique signature, which is hard to detect with algorithms and objective features. In this paper we propose a novel dictionary learning approach to automatically uncover the artistic style from paintings. Specifically, we present a multi-task learning algorithm to learn a style-specific dictionary representation. Intuitively, our approach, by automatically decoupling style-specific and artist-specific patterns, is expected to be more accurate for retrieval and recognition tasks than generic methods. To demonstrate the effectiveness of our approach, we introduce the DART dataset, containing more than 1.5K images of paintings representative of different styles. Our extensive experimental evaluation shows that our approach significantly outperforms state-of-the-art methods.

Inferring painting style with multi-task dictionary learning / Liu, Gaowen; Yan, Yan; Ricci, E.; Yang, Yi; Han, Y.; Winkler, S.; Sebe, Niculae. - (2015), pp. 2162-2168. ((Intervento presentato al convegno IJCAI 2015 tenutosi a Buenos Aires nel 25-31 July, 2015 [http://dl.acm.org/citation.cfm?id=2832549].

Inferring painting style with multi-task dictionary learning

Liu, Gaowen;Yan, Yan;Ricci, E.;Sebe, Niculae
2015

Abstract

Recent advances in imaging and multimedia technologies have paved the way for automatic analysis of visual art. Despite notable attempts, extracting relevant patterns from paintings is still a challenging task. Different painters, born in different periods and places, have been influenced by different schools of arts. However, each individual artist also has a unique signature, which is hard to detect with algorithms and objective features. In this paper we propose a novel dictionary learning approach to automatically uncover the artistic style from paintings. Specifically, we present a multi-task learning algorithm to learn a style-specific dictionary representation. Intuitively, our approach, by automatically decoupling style-specific and artist-specific patterns, is expected to be more accurate for retrieval and recognition tasks than generic methods. To demonstrate the effectiveness of our approach, we introduce the DART dataset, containing more than 1.5K images of paintings representative of different styles. Our extensive experimental evaluation shows that our approach significantly outperforms state-of-the-art methods.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence
Palo Alto, CA
AAAI Press
978-1-57735-738-4
Liu, Gaowen; Yan, Yan; Ricci, E.; Yang, Yi; Han, Y.; Winkler, S.; Sebe, Niculae
Inferring painting style with multi-task dictionary learning / Liu, Gaowen; Yan, Yan; Ricci, E.; Yang, Yi; Han, Y.; Winkler, S.; Sebe, Niculae. - (2015), pp. 2162-2168. ((Intervento presentato al convegno IJCAI 2015 tenutosi a Buenos Aires nel 25-31 July, 2015 [http://dl.acm.org/citation.cfm?id=2832549].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/115065
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