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DisLocation: Scalable Descriptor Distinctiveness for Location Recognition

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9006))

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Abstract

The objective of this paper is to improve large scale visual object retrieval for visual place recognition. Geo-localization based on a visual query is made difficult by plenty of non-distinctive features which commonly occur in imagery of urban environments, such as generic modern windows, doors, cars, trees, etc. The focus of this work is to adapt standard Hamming Embedding retrieval system to account for varying descriptor distinctiveness. To this end, we propose a novel method for efficiently estimating distinctiveness of all database descriptors, based on estimating local descriptor density everywhere in the descriptor space. In contrast to all competing methods, the (unsupervised) training time for our method (DisLoc) is linear in the number database descriptors and takes only a 100 s on a single CPU core for a 1 million image database. Furthermore, the added memory requirements are negligible (1 %).

The method is evaluated on standard publicly available large-scale place recognition benchmarks containing street-view imagery of Pittsburgh and San Francisco. DisLoc is shown to outperform all baselines, while setting the new state-of-the-art on both benchmarks. The method is compatible with spatial reranking, which further improves recognition results.

Finally, we also demonstrate that 7 % of the least distinctive features can be removed, therefore reducing storage requirements and improving retrieval speed, without any loss in place recognition accuracy.

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Acknowledgement

We thank A. Torri and D. Chen for sharing their datasets, and are grateful for financial support from ERC grant VisRec no. 228180 and a Royal Society Wolfson Research Merit Award.

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Authors and Affiliations

  1. Department of Engineering Science, University of Oxford, Oxford, UK

    Relja Arandjelović & Andrew Zisserman

Authors
  1. Relja Arandjelović
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  2. Andrew Zisserman
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Corresponding author

Correspondence to Relja Arandjelović .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Arandjelović, R., Zisserman, A. (2015). DisLocation: Scalable Descriptor Distinctiveness for Location Recognition. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_13

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  • DOI: https://doi.org/10.1007/978-3-319-16817-3_13

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