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Abstract

In this paper we report on our successful participation in the RobotVision challenge in the ImageCLEF 2009 campaign. We present a place recognition system that employs four different discriminative models trained on different global and local visual cues. In order to provide robust recognition, the outputs generated by the models are combined using a discriminative accumulation method. Moreover, the system is able to provide an indication of the confidence of its decision. We analyse the properties and performance of the system on the training and validation data and report the final score obtained on the test run which ranked first in the obligatory track of the RobotVision task.

Keywords

Interest Point Scale Invariant Feature Transform Temporal Accumulation Optional Track Place Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Li Xing
    • 1
  • Andrzej Pronobis
    • 1
  1. 1.Centre for Autonomous SystemsThe Royal Institute of TechnologyStockholmSweden

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