Multispectral SIFT for Scene Category Recognition

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.


Published in:
Proc. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2011), 177-184
Presented at:
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2011), Colorado Springs, CO, USA, June 2011
Year:
2011
Publisher:
IEEE Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa
Keywords:
Note:
The RGB-NIR database used in this paper can be downloaded from http://ivrg.epfl.ch/supplementary_material/cvpr11/index.html
Laboratories:




 Record created 2011-07-06, last modified 2018-03-18

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