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Visual place recognition using landmark distribution descriptors

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2016
Subtitle of host publication13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers
Publisher or commissioning bodySpringer-Verlag Berlin
Number of pages16
Volume10114 LNCS
ISBN (Print)9783319541891
DateAccepted/In press - 14 Aug 2016
DateE-pub ahead of print - 12 Mar 2017
DatePublished (current) - 2017
Event13th Asian Conference on Computer Vision (ACCV’16) - Taipei, Taiwan
Duration: 20 Nov 201624 Nov 2016
Conference number: 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10114 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference13th Asian Conference on Computer Vision (ACCV’16)
Abbreviated titleACCV’16
Internet address


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].


13th Asian Conference on Computer Vision (ACCV’16)

Abbreviated titleACCV’16
Conference number13
Duration20 Nov 201624 Nov 2016
Web address (URL)

Event: Conference

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    Accepted author manuscript, 3 MB, PDF-document


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