SUN database: Large-scale scene recognition from abbey to zoo
Open accessAbstract
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.
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The sparse-sampling strategy causes contents within frames to be less correlated, especially for long videos, implying that feature distributions are quite different.
“Firstly, the sparse-sampling strategy is usually adopted when tackling video representation learning to reduce the training costs [21].The contents within those frames are less correlated, especially for the cases of long videos, implying the features in terms of distributions are quite different [22], [23].”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [23]
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- Authors
- Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A.
- Affiliations
- Massachusetts Institute of Technology (US)
- Brown University (US)
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 3485-3492
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2017814585
- Cited by
- 3,283
- References
- 5
- Citations by year
- 2026: 186 · 2025: 298 · 2024: 311 · 2023: 255 · 2022: 213 · 2021: 248 · 2020: 209 · 2019: 247 · 2018: 214 · 2017: 199 · 2016: 250 · 2015: 225 · 2014: 181 · 2013: 125 · 2012: 74
- FWCI
- 67.50
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-sa
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- scene classification, scene understanding, human performance comparison
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