Multi-spectral saliency detection
Pattern Recognition Letters
A new biologically inspired color image descriptor
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Semantic image segmentation using visible and near-infrared channels
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Multi-spectral dataset and its application in saliency detection
Computer Vision and Image Understanding
Robust blind motion deblurring using near-infrared flash image
Journal of Visual Communication and Image Representation
Similarity-based clustering by left-stochastic matrix factorization
The Journal of Machine Learning Research
Hi-index | 0.00 |
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.