On the location dependence of convolutional neural network features

Scott Workman, Nathan Jacobs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

80 Scopus citations

Abstract

As the availability of geotagged imagery has increased, so has the interest in geolocation-related computer vision applications, ranging from wide-area image geolocalization to the extraction of environmental data from social network imagery. Encouraged by the recent success of deep convolutional networks for learning high-level features, we investigate the usefulness of deep learned features for such problems. We compare features extracted from various layers of convolutional neural networks and analyze their discriminative ability with regards to location. Our analysis spans several problem settings, including region identification, visualizing land cover in aerial imagery, and ground-image localization in regions without ground-image reference data (where we achieve state-of-the-art performance on a benchmark dataset). We present results on multiple datasets, including a new dataset we introduce containing hundreds of thousands of ground-level and aerial images in a large region centered around San Francisco.

Original languageEnglish
Title of host publication2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015
Pages70-78
Number of pages9
ISBN (Electronic)9781467367592
DOIs
StatePublished - Oct 19 2015
EventIEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015 - Boston, United States
Duration: Jun 7 2015Jun 12 2015

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2015-October
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015
Country/TerritoryUnited States
CityBoston
Period6/7/156/12/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Databases
  • Feature extraction
  • Geology
  • Neural networks
  • Principal component analysis
  • Support vector machines
  • Visualization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'On the location dependence of convolutional neural network features'. Together they form a unique fingerprint.

Cite this