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Visual Place Recognition Using Landmark Distribution Descriptors

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10114))

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Abstract

Recent work by Sünderhauf et al. [1] demonstrated improved visual place recognition using proposal regions coupled with features from convolutional neural networks (CNN) to match landmarks between views. In this work we extend the approach by introducing descriptors built from landmark features which also encode the spatial distribution of the landmarks within a view. Matching descriptors then enforces consistency of the relative positions of landmarks between views. This has a significant impact on performance. For example, in experiments on 10 image-pair datasets, each consisting of 200 urban locations with significant differences in viewing positions and conditions, we recorded average precision of around 70% (at 100% recall), compared with 58% obtained using whole image CNN features and 50% for the method in [1].

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Notes

  1. 1.

    For clarity with respect to our experiments, we should note that we found that the similarity metric provided in [1] did not give good performance and so in the interests of fairness we used a modified version which gave significantly better performance. Specifically, we modified Eqs. (2) and (3) in [1] to be \(s_{ij} = 1 - (\frac{1}{2} (\frac{|w_{i} - w_{j}|}{max(w_{i},w_{j})} + \frac{|h_{i} - h_{j}|}{max(h_{i},h_{j})}))\) and \(S_{ab} = \frac{1}{n_{a} \cdot n_{b}} \sum _{ij} (d_{ij} \cdot s_{ij}))\), respectively.

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Authors and Affiliations

  1. Department of Computer Science, University of Bristol, Bristol, UK

    Pilailuck Panphattarasap & Andrew Calway

Authors
  1. Pilailuck Panphattarasap
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  2. Andrew Calway
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Corresponding author

Correspondence to Pilailuck Panphattarasap .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Panphattarasap, P., Calway, A. (2017). Visual Place Recognition Using Landmark Distribution Descriptors. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10114. Springer, Cham. https://doi.org/10.1007/978-3-319-54190-7_30

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  • DOI: https://doi.org/10.1007/978-3-319-54190-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54189-1

  • Online ISBN: 978-3-319-54190-7

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