Back to articles
Articles
Volume: 32 | Article ID: art00024
Image
Multiscale Convolutional Descriptor Aggregation for Visual Place Recognition
  DOI :  10.2352/ISSN.2470-1173.2020.10.IPAS-313  Published OnlineJanuary 2020
Abstract

Visual place recognition using query and database images from different sources remains a challenging task in computer vision. Our method exploits global descriptors for efficient image matching and local descriptors for geometric verification. We present a novel, multi-scale aggregation method for local convolutional descriptors, using memory vector construction for efficient aggregation. The method enables to find preliminary set of image candidate matches and remove visually similar but erroneous candidates. We deploy the multi-scale aggregation for visual place recognition on 3 large-scale datasets. We obtain a Recall@10 larger than 94% for the Pittsburgh dataset, outperforming other popular convolutional descriptors used in image retrieval and place recognition. Additionally, we provide a comparison for these descriptors on a more challenging dataset containing query and database images obtained from different sources, achieving over 77% Recall@10.

Subject Areas :
Views 11
Downloads 0
 articleview.views 11
 articleview.downloads 0
  Cite this article 

Raffaele Imbriaco, Egor Bondarev, Peter H.N. de With, "Multiscale Convolutional Descriptor Aggregation for Visual Place Recognitionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVIII,  2020,  pp 313-1 - 313-7,  https://doi.org/10.2352/ISSN.2470-1173.2020.10.IPAS-313

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2020
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA