Multi-spectral SIFT for scene category recognition

Matthew A Brown, S Susstrunk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

207 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011)
PublisherIEEE
Pages177-184
Number of pages8
ISBN (Print)9781457703942
DOIs
Publication statusPublished - Jul 2011
EventIEEE Computer Vision and Pattern Recognition (CVPR) 2011 - Colorado Springs
Duration: 21 Jun 201125 Jun 2011

Conference

ConferenceIEEE Computer Vision and Pattern Recognition (CVPR) 2011
CityColorado Springs
Period21/06/1125/06/11

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  • Cite this

    Brown, M. A., & Susstrunk, S. (2011). Multi-spectral SIFT for scene category recognition. In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011) (pp. 177-184). IEEE. https://doi.org/10.1109/CVPR.2011.5995637