SUN database: Large-scale scene recognition from abbey to zoo
Name
Oliva_SUN database.pdf
Size
8.22 MB
Format
Adobe PDF
Checksum (MD5)
1c72dabf330e18b813d0444f3fdb6b61
Author(s)
Xiao, Jianxiong
Hays, James
Ehinger, Krista A.
Oliva, Aude
Torralba, Antonio
Date Issued
August 2010
Journal
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2010. CVPR 2010.
Publisher
Institute of Electrical and Electronics Engineers
Citation
Jianxiong Xiao et al. “SUN database: Large-scale scene recognition from abbey to zoo.” Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. 2010. 3485-3492.
Version
Author's final manuscript
Abstract
Scene categorization is a fundamental problem in computer vision. However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes. In this paper we propose the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images. We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance. We measure human scene classification performance on the SUN database and compare this with computational methods. Additionally, we study a finer-grained scene representation to detect scenes embedded inside of larger scenes.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Attribution-Noncommercial-Share Alike 3.0 Unported
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPR.2010.5539970
