Learning geo-temporal image features

Menghua Zhai, Tawfiq Salem, Connor Greenwell, Scott Workman, Nathan Jacobs, Robert Pless

Research output: Contribution to conferencePaperpeer-review

9 Scopus citations

Abstract

We propose to implicitly learn to extract geo-temporal image features, which are mid-level features related to when and where an image was captured, by explicitly optimizing for a set of location and time estimation tasks. To train our method, we take advantage of a large image dataset, captured by outdoor webcams and cell phones. The only form of supervision we provide are the known capture time and location of each image. We find that our approach learns features that are related to natural appearance changes in outdoor scenes. Additionally, we demonstrate the application of these geo-temporal features to time and location estimation.

Original languageEnglish
StatePublished - 2019
Event29th British Machine Vision Conference, BMVC 2018 - Newcastle, United Kingdom
Duration: Sep 3 2018Sep 6 2018

Conference

Conference29th British Machine Vision Conference, BMVC 2018
Country/TerritoryUnited Kingdom
CityNewcastle
Period9/3/189/6/18

Bibliographical note

Publisher Copyright:
© 2018. The copyright of this document resides with its authors.

Funding

We gratefully acknowledge the support of NSF CAREER award IIS-1553116 and ARPA-E Award DE-AR0000594. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

FundersFunder number
National Science Foundation (NSF)IIS-1553116
Advanced Research Projects Agency - EnergyDE-AR0000594

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

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