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.
This work was supported by the EU FP7 integrated project ICT-215181-CogX. The support is gratefully acknowledged.
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Xing, L., Pronobis, A. (2010). Multi-cue Discriminative Place Recognition. In: Peters, C., et al. Multilingual Information Access Evaluation II. Multimedia Experiments. CLEF 2009. Lecture Notes in Computer Science, vol 6242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15751-6_41
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DOI: https://doi.org/10.1007/978-3-642-15751-6_41
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