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.
|Title of host publication||2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011)|
|Number of pages||8|
|Publication status||Published - Jul 2011|
|Event||IEEE Computer Vision and Pattern Recognition (CVPR) 2011 - Colorado Springs|
Duration: 21 Jun 2011 → 25 Jun 2011
|Conference||IEEE Computer Vision and Pattern Recognition (CVPR) 2011|
|Period||21/06/11 → 25/06/11|