The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Multi-spectral SIFT for scene category recognition
Open accessAbstract
We use a simple modification to a conventional SLR camera to capture images of several hundred scenes in colour (RGB) and near-infrared (NIR). We show that the addition of near-infrared information leads to significantly improved performance in a scene-recognition task, and that the improvements are greater still when an appropriate 4-dimensional colour representation is used. In particular we propose MSIFT - a multispectral SIFT descriptor that, when combined with a kernel based classifier, exceeds the performance of state-of-the-art scene recognition techniques (e.g., GIST) and their multispectral extensions. We extensively test our algorithms using a new dataset of several hundred RGB-NIR scene images, as well as benchmarking against Torralba's scene categorization dataset.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Oliva, A., & Torralba, A. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,319
Evaluating Color Descriptors for Object and Scene Recognition
van de Sande, K. E. A., Gevers, T., & Snoek, C. G. M. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.154 · Cited by 1,783
Image Alignment and Stitching: A Tutorial
Szeliski, R. (2007) · Foundations and Trends in Computer Graphics and Vision · doi:10.1561/0600000009 · Cited by 1,593
In defense of Nearest-Neighbor based image classification
Boiman, O., Shechtman, E., & Irani, M. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587598 · Cited by 1,072
Contextual Priming for Object Detection
Torralba, A. (2003) · International Journal of Computer Vision · doi:10.1023/a:1023052124951 · Cited by 776
A novel algorithm for color constancy
Forsyth, D. A. (1990) · International Journal of Computer Vision · doi:10.1007/bf00056770 · Cited by 722
From Blobs to Boundary Edges: Evidence for Time- and Spatial-Scale-Dependent Scene Recognition
Schyns, P. G., & Oliva, A. (1994) · Psychological Science · doi:10.1111/j.1467-9280.1994.tb00500.x · Cited by 715
Statistics of cone responses to natural images: implications for visual coding
Ruderman, D. L., Cronin, T. W., & Chiao, C.-C. (1998) · Journal of the Optical Society of America A · doi:10.1364/josaa.15.002036 · Cited by 715
Geusebroek, J.-M., van den Boomgaard, R., Smeulders, A. W. M., & Geerts, H. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.977559 · Cited by 553
Cited by
SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer
Ma, J., Tang, L., Fan, F., Huang, J., Mei, X., & Ma, Y. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105686 · Cited by 1,526
A review of multimodal image matching: Methods and applications
Jiang, X., Ma, J., Xiao, G., Shao, Z., & Guo, X. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.012 · Cited by 532
Gao, J., Yang, J., Wang, G., & Li, M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.06.055 · Cited by 44
Metadata
- Authors
- Brown, M., & Susstrunk, S.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 177-184
- OpenAlex
- W2012893378
- Cited by
- 469
- References
- 25
- FWCI
- 11.24
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Multi-spectral SIFT for scene category recognition. https://app.cochat.ai/papers/10.1109/cvpr.2011.5995637
- The paper
- Brown, M., & Susstrunk, S. (2011). Multi-spectral SIFT for scene category recognition. CVPR 2011. https://doi.org/10.1109/cvpr.2011.5995637
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.