Visual Place Recognition with Repetitive Structures

Akihiko Torii 1 Josef Sivic 2, 3 Tomas Pajdla 4 Masatoshi Okutomi 1
2 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : Repeated structures such as building facades, fences or road markings often represent a significant challenge for place recognition. Repeated structures are notoriously hard for establishing correspondences using multi-view geometry. Even more importantly, they violate the feature independence assumed in the bag-of-visual-words representation which often leads to over-counting evidence and significant degradation of retrieval performance. In this work we show that repeated structures are not a nuisance but, when appropriately represented, they form an important distinguishing feature for many places. We describe a representation of repeated structures suitable for scalable retrieval. It is based on robust detection of repeated image structures and a simple modification of weights in the bag-of-visual-word model. Place recognition results are shown on datasets of street-level imagery from Pittsburgh and San Francisco demonstrating significant gains in recognition performance compared to the standard bag-of-visual-words baseline and more recently proposed burstiness weighting.
Type de document :
Communication dans un congrès
CVPR 2013 - 26th IEEE Conference on Computer Vision and Pattern Recognition, Jun 2013, Portland, United States. IEEE, 2013
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Contributeur : Josef Sivic <>
Soumis le : mardi 21 janvier 2014 - 17:29:49
Dernière modification le : jeudi 7 février 2019 - 15:49:48
Document(s) archivé(s) le : mardi 22 avril 2014 - 10:01:55


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  • HAL Id : hal-00934288, version 1



Akihiko Torii, Josef Sivic, Tomas Pajdla, Masatoshi Okutomi. Visual Place Recognition with Repetitive Structures. CVPR 2013 - 26th IEEE Conference on Computer Vision and Pattern Recognition, Jun 2013, Portland, United States. IEEE, 2013. 〈hal-00934288〉



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