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State of Art Survey On: Large Scale Image Location Recognition

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Computational Science and Its Applications – ICCSA 2016 (ICCSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9790))

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Abstract

Image location recognition is a well known process of retrieving the precise location from the contents of the photographs. New photographs are compared to a large geocoded database and the result is both the position and orientation.

This is an inexpensive location system, since it only needs a camera, present in all modern smart phones. It has some great advantages over GPS. It give us the orientation and works under occluded environments, making this system highly attractive to a wide variety of applications.

But at a large scale, this process is easily hindered by heavy weighted database representations, expensive computational operations and visually similar environments. As a consequence, low geocoding rates, inaccurate localization and slow queries are obtained.

In the past years, a variety of solutions have been proposed to address these challenges but we are yet to adopt one of them as the image location recognition solution.

In this paper we review and compare recent state of art advances on image geocoding algorithms focusing on the scalability of such solutions.

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Notes

  1. 1.

    https://www.flickr.com/.

  2. 2.

    http://www.panoramio.com/.

  3. 3.

    https://www.google.com/maps/streetview/.

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Authors and Affiliations

  1. Algoritmi Research Centre, University of Minho, 4710-057, Braga, Portugal

    Nuno Amorim & Jorge Gustavo Rocha

Authors
  1. Nuno Amorim
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  2. Jorge Gustavo Rocha
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Corresponding author

Correspondence to Nuno Amorim .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  5. Polytechnic University, Bari, Italy

    Carmelo M. Torre

  6. Monash University, Clayton, Victoria, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  9. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

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Amorim, N., Rocha, J.G. (2016). State of Art Survey On: Large Scale Image Location Recognition. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9790. Springer, Cham. https://doi.org/10.1007/978-3-319-42092-9_29

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  • DOI: https://doi.org/10.1007/978-3-319-42092-9_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42091-2

  • Online ISBN: 978-3-319-42092-9

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