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 language | English |
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Title of host publication | 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011) |
Publisher | IEEE |
Pages | 177-184 |
Number of pages | 8 |
ISBN (Print) | 9781457703942 |
DOIs | |
Publication status | Published - Jul 2011 |
Event | IEEE Computer Vision and Pattern Recognition (CVPR) 2011 - Colorado Springs Duration: 21 Jun 2011 → 25 Jun 2011 |
Conference
Conference | IEEE Computer Vision and Pattern Recognition (CVPR) 2011 |
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City | Colorado Springs |
Period | 21/06/11 → 25/06/11 |