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Combining Holistic and Part-based Deep Representations for Computational Painting Categorization
Aalto University, Finland.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
University of Autonoma Barcelona, Spain.
Aalto University, Finland.
2016 (English)In: ICMR16: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, ASSOC COMPUTING MACHINERY , 2016, 339-342 p.Conference paper, Published paper (Refereed)
Abstract [en]

Automatic analysis of visual art, such as paintings, is a challenging inter-disciplinary research problem. Conventional approaches only rely on global scene characteristics by encoding holistic information for computational painting categorization. We argue that such approaches are sub-optimal and that discriminative common visual structures provide complementary information for painting classification. We present an approach that encodes both the global scene layout and discriminative latent common structures for computational painting categorization. The region of interests are automatically extracted, without any manual part labeling, by training class-specific deformable part-based models. Both holistic and region-of-interests are then described using multi-scale dense convolutional features. These features are pooled separately using Fisher vector encoding and concatenated afterwards in a single image representation. Experiments are performed on a challenging dataset with 91 different painters and 13 diverse painting styles. Our approach outperforms the standard method, which only employs the global scene characteristics. Furthermore, our method achieves state-of-the-art results outperforming a recent multi-scale deep features based approach [11] by 6.4% and 3.8% respectively on artist and style classification.

Place, publisher, year, edition, pages
ASSOC COMPUTING MACHINERY , 2016. 339-342 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-133018DOI: 10.1145/2911996.2912063ISI: 000387613700054ISBN: 978-1-4503-4359-6 (print)OAI: oai:DiVA.org:liu-133018DiVA: diva2:1054664
Conference
ACM International Conference on Multimedia Retrieval (ICMR)
Available from: 2016-12-08 Created: 2016-12-07 Last updated: 2016-12-08

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Khan, Fahad
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf