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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 access
Brown, Matthew, Susstrunk, Sabine·2011·CVPR 2011

Abstract

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.

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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

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CoChat. (2026). Citation check: Multi-spectral SIFT for scene category recognition. https://app.cochat.ai/papers/10.1109/cvpr.2011.5995637
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Brown, M., & Susstrunk, S. (2011). Multi-spectral SIFT for scene category recognition. CVPR 2011. https://doi.org/10.1109/cvpr.2011.5995637

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