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Where the Photos Were Taken: Location Prediction by Learning from Flickr Photos

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Large-Scale Visual Geo-Localization

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

In this chapter, we explore the characteristics of geographically tagged Internet photos and determine their location based on the visual content. We develop a principled machine learning model to estimate geographical locations of photos by modeling the relationship between location and the photo content. To build reliable geographical estimators, it is important to find distinguishable geographical clusters in the world. These clusters cover general geographical regions not limited to just landmarks. Geographical clusters provide more training samples and hence lead to better recognition accuracy. We develop a framework for geographical cluster estimation, and employ latent variables to estimate the geographical clusters. To solve this estimation problem, we propose to build an efficient solver to find the latent clusters. We illustrate detailed qualitative results obtained from beaches photos taken at different continents. In addition, we show significantly improved quantitative results over other approaches for recognizing different beaches using the Flickr beach dataset as validation.

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Author information

Authors and Affiliations

  1. Yahoo! Research, Sunnyvale, USA

    Li-Jia Li

  2. University of Michigan, Ann Arbor, USA

    Rahul Kumar Jha

  3. Yahoo! Research, San Francisco, USA

    Bart Thomee & David Ayman Shamma

  4. IBM Watson Research, New York, USA

    Liangliang Cao

  5. University of Manitoba, Winnipeg, Canada

    Yang Wang

Authors
  1. Li-Jia Li
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  2. Rahul Kumar Jha
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  3. Bart Thomee
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  4. David Ayman Shamma
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  5. Liangliang Cao
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  6. Yang Wang
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Corresponding author

Correspondence to Li-Jia Li .

Editor information

Editors and Affiliations

  1. Computer Science Department, Stanford University Computer Science Department, Stanford, California, USA

    Amir R. Zamir

  2. Decisive Analytics Corporation, Arlington, Virginia, USA

    Asaad Hakeem

  3. ETH Zürich, Zürich, Switzerland

    Luc Van Gool

  4. University of Central Florida, Orlando, Florida, USA

    Mubarak Shah

  5. Facebook, Seattle, Washington, USA

    Richard Szeliski

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© 2016 Springer International Publishing Switzerland

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Li, LJ., Jha, R.K., Thomee, B., Shamma, D.A., Cao, L., Wang, Y. (2016). Where the Photos Were Taken: Location Prediction by Learning from Flickr Photos. In: Zamir, A., Hakeem, A., Van Gool, L., Shah, M., Szeliski, R. (eds) Large-Scale Visual Geo-Localization. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-25781-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-25781-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25779-2

  • Online ISBN: 978-3-319-25781-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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