Multiscale Convolutional Descriptor Aggregation for Visual Place Recognition

Research output: Contribution to journalConference articleAcademicpeer-review

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.

Original languageEnglish
Article number313
Number of pages7
JournalIS and T International Symposium on Electronic Imaging Science and Technology
Volume2020
Issue number10
DOIs
Publication statusPublished - 26 Jan 2020
Event18th Image Processing: Algorithms and Systems Conference, IPAS 2020 - Burlingame, United States
Duration: 26 Jan 202030 Jan 2020

Fingerprint Dive into the research topics of 'Multiscale Convolutional Descriptor Aggregation for Visual Place Recognition'. Together they form a unique fingerprint.

Cite this