logo Idiap Research Institute        
 [BibTeX] [Marc21]
Indoor Place Recognition using Online Independent Support Vector Machines
Type of publication: Conference paper
Citation: orabona:bmvc:2007
Booktitle: 18th British Machine Vision Conference (BMVC07)
Year: 2007
Month: 9
Address: Warwick, UK
Abstract: In the framework of indoor mobile robotics, place recognition is a challenging task, where it is crucial that self-localization be enforced precisely, notwithstanding the changing conditions of illumination, objects being shifted around and/or people affecting the appearance of the scene. In this scenario online learning seems the main way out, thanks to the possibility of adapting to changes in a smart and flexible way. Nevertheless, standard machine learning approaches usually suffer when confronted with massive amounts of data and when asked to work online. Online learning requires a high training and testing speed, all the more in place recognition, where a continuous flow of data comes from one or more cameras. In this paper we follow the Support Vector Machines-based approach of Pronobis et al., proposing an improvement that we call Online Independent Support Vector Machines. This technique exploits linear independence in the image feature space to incrementally keep the size of the learning machine remarkably small while retaining the accuracy of a standard machine. Since the training and testing time crucially depend on the size of the machine, this solves the above stated problems. Our experimental results prove the effectiveness of the approach.
Userfields: ipdmembership={Vision},
Projects Idiap
Authors Orabona, Francesco
Castellini, Claudio
Caputo, Barbara
Luo, Jie
Sandini, Giulio
Added by: [UNK]
Total mark: 0
  • orabona_bmvc07.pdf
  • orabona_bmvc07.ps.gz