Visual Place Recognition with Repetitive Structures

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. They violate the feature independence assumed in the bag-of-visual...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 37; číslo 11; s. 2346 - 2359
Hlavní autori:	Torii, Akihiko, Sivic, Josef, Okutomi, Masatoshi, Pajdla, Tomas
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.11.2015 Institute of Electrical and Electronics Engineers
Predmet:	Bag of Visual Words Buildings Computer Science Computer Vision and Pattern Recognition Feature extraction Image recognition Image Retrieval Indexing Place Recognition Vectors Visual databases Visualization image retrieval Place recognition bag of visual words geometric verification Place Recognition Geometric Verification Image Retrieval Bag of Visual Words
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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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. 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 and geometric verification. The retrieval is based on robust detection of repeated image structures and a suitable modification of weights in the bag-of-visual-word model. We also demonstrate that the explicit detection of repeated patterns is beneficial for robust visual word matching for geometric verification. 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 as well as the more recently proposed burstiness weighting and Fisher vector encoding.
AbstractList	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. 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 and geometric verification. The retrieval is based on robust detection of repeated image structures and a suitable modification of weights in the bag-of-visual-word model. We also demonstrate that the explicit detection of repeated patterns is beneficial for robust visual word matching for geometric verification. 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 as well as the more recently proposed burstiness weighting and Fisher vector encoding. 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. 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 and geometric verification. The retrieval is based on robust detection of repeated image structures and a suitable modification of weights in the bag-of-visual-word model. We also demonstrate that the explicit detection of repeated patterns is beneficial for robust visual word matching for geometric verification. 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 as well as the more recently proposed burstiness weighting and Fisher vector encoding.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. 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 and geometric verification. The retrieval is based on robust detection of repeated image structures and a suitable modification of weights in the bag-of-visual-word model. We also demonstrate that the explicit detection of repeated patterns is beneficial for robust visual word matching for geometric verification. 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 as well as the more recently proposed burstiness weighting and Fisher vector encoding.
Author	Torii, Akihiko Okutomi, Masatoshi Sivic, Josef Pajdla, Tomas
Author_xml	– sequence: 1 givenname: Akihiko surname: Torii fullname: Torii, Akihiko email: torii@ctrl.titech.ac.jp organization: Dept. of Mech. & Control Eng., Tokyo Inst. of Technol., Tokyo, Japan – sequence: 2 givenname: Josef surname: Sivic fullname: Sivic, Josef email: Josef.Sivic@ens.fr organization: Dept. d'Inf., Ecole Normale Super., Paris, France – sequence: 3 givenname: Masatoshi surname: Okutomi fullname: Okutomi, Masatoshi email: mxo@ctrl.titech.ac.jp organization: Dept. of Mech. & Control Eng., Tokyo Inst. of Technol., Tokyo, Japan – sequence: 4 givenname: Tomas surname: Pajdla fullname: Pajdla, Tomas email: pajdla@cmp.felk.cvut.cz organization: Dept. of Cybern., Czech Tech. Univ. in Prague, Prague, Czech Republic
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Snippet	Repeated structures such as building facades, fences or road markings often represent a significant challenge for place recognition. Repeated structures are...
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SubjectTerms	Bag of Visual Words Buildings Computer Science Computer Vision and Pattern Recognition Feature extraction Image recognition Image Retrieval Indexing Place Recognition Vectors Visual databases Visualization
Title	Visual Place Recognition with Repetitive Structures
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