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Indoor Scene Classification Using Combined 3D and Gist Features

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Computer Vision – ACCV 2010 (ACCV 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6493))

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Abstract

Scene categorization is an important mechanism for providing high-level context which can guide methods for a more detailed analysis of scenes. State-of-the-art techniques like Torralba’s Gist features show a good performance on categorizing outdoor scenes but have problems in categorizing indoor scenes. In contrast to object based approaches, we propose a 3D feature vector capturing general properties of the spatial layout of indoor scenes like shape and size of extracted planar patches and their orientation to each other. This idea is supported by psychological experiments which give evidence for the special role of 3D geometry in categorizing indoor scenes. In order to study the influence of the 3D geometry we introduce in this paper a novel 3D indoor database and a method for defining 3D features on planar surfaces extracted in 3D data. Additionally, we propose a voting technique to fuse 3D features and 2D Gist features and show in our experiments a significant contribution of the 3D features to the indoor scene categorization task.

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Author information

Authors and Affiliations

  1. Applied Informatics, Bielefeld University, Germany

    Agnes Swadzba & Sven Wachsmuth

Authors
  1. Agnes Swadzba
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  2. Sven Wachsmuth
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Technion, Israel Institute of Technology, 32000, Haifa, Israel

    Ron Kimmel

  2. The University of Auckland, 37 Kohimarama Road, Mission Bay, 1071, Auckland, New Zealand

    Reinhard Klette

  3. National Institute of Informatics, 1018430, Chiyoda, Tokyo, Japan

    Akihiro Sugimoto

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Swadzba, A., Wachsmuth, S. (2011). Indoor Scene Classification Using Combined 3D and Gist Features. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_16

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  • DOI: https://doi.org/10.1007/978-3-642-19309-5_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19308-8

  • Online ISBN: 978-3-642-19309-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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