Indoor Scene Classification using combined 3D and Gist Features

Swadzba A, Wachsmuth S (2011)
In: Computer Vision - ACCV 2010. Lecture Notes in Computer Science, 6493. Berlin, Heidelberg: Springer: 201-215.

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Konferenzbeitrag | Veröffentlicht | Englisch
Abstract / Bemerkung
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 in uence of the 3D geometry we introduce in this paper a novel 3D indoor database and a method for de ning 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 signi cant contribution of the 3D features to the indoor scene categorization task.
Erscheinungsjahr
Titel des Konferenzbandes
Computer Vision - ACCV 2010
Seite
201-215
Konferenz
Asian Conference on Computer Vision
Konferenzort
Queenstown, New Zealand
Konferenzdatum
2010-11-08 – 2010-11-12
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Swadzba A, Wachsmuth S. Indoor Scene Classification using combined 3D and Gist Features. In: Computer Vision - ACCV 2010. Lecture Notes in Computer Science, 6493. Berlin, Heidelberg: Springer; 2011: 201-215.
Swadzba, A., & Wachsmuth, S. (2011). Indoor Scene Classification using combined 3D and Gist Features. Computer Vision - ACCV 2010, Lecture Notes in Computer Science, 6493, 201-215. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-19309-5_16
Swadzba, A., and Wachsmuth, S. (2011). “Indoor Scene Classification using combined 3D and Gist Features” in Computer Vision - ACCV 2010 Lecture Notes in Computer Science, 6493 (Berlin, Heidelberg: Springer), 201-215.
Swadzba, A., & Wachsmuth, S., 2011. Indoor Scene Classification using combined 3D and Gist Features. In Computer Vision - ACCV 2010. Lecture Notes in Computer Science, 6493. Berlin, Heidelberg: Springer, pp. 201-215.
A. Swadzba and S. Wachsmuth, “Indoor Scene Classification using combined 3D and Gist Features”, Computer Vision - ACCV 2010, Lecture Notes in Computer Science, 6493, Berlin, Heidelberg: Springer, 2011, pp.201-215.
Swadzba, A., Wachsmuth, S.: Indoor Scene Classification using combined 3D and Gist Features. Computer Vision - ACCV 2010. Lecture Notes in Computer Science, 6493. p. 201-215. Springer, Berlin, Heidelberg (2011).
Swadzba, Agnes, and Wachsmuth, Sven. “Indoor Scene Classification using combined 3D and Gist Features”. Computer Vision - ACCV 2010. Berlin, Heidelberg: Springer, 2011. Lecture Notes in Computer Science, 6493. 201-215.
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2017-02-01T08:27:49Z

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