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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Published inIEEE transactions on pattern analysis and machine intelligence Vol. 37; no. 11; pp. 2346 - 2359
Main Authors Torii, Akihiko, Sivic, Josef, Okutomi, Masatoshi, Pajdla, Tomas
Format Journal Article
LanguageEnglish
Published United States IEEE 01.11.2015
Institute of Electrical and Electronics Engineers
Subjects
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
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Summary: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.
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ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2015.2409868