Multi-spectral 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.

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Matthew Brown, Sabine Susstrunk
• Keywords: Appropriate 4-dimensional Colour, Computer Vision, Conventional Slr Camera, CVPR 2011, Multispectral Sift Descriptor