PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combining Visible and Near-Infrared Cues for Image Categorisation
Neda Salamati, Diane Larlus and Gabriela Csurka
British Machine Vision Conference 49.1-49.11, 2011. ISSN 1-901725-43-X

Abstract

Standard digital cameras are sensitive to radiation in the near-infrared domain, but this additional cue is in general discarded. In this paper, we consider the scene categorisation problem in the context of images where both standard visible RGB channels and near infrared information are available. Using efficient local patch-based Fisher Vector image representations, we show based on thorough experimental studies the benefit of using this new type of data. We investigate which image descriptors are relevant, and how to best combine them. In particular, our experiments show that when combining texture and colour information, computed on visible and near-infrared channels, late fusion is the best performing strategy and outperforms the state-of-the-art categorisation methods on RGB-only data.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:8948
Deposited By:Diane Larlus
Deposited On:21 February 2012