Location Estimation of a Photo: A Geo-signature MapReduce Workflow

Authors

  • Pakpoom Mookdarsanit King Mongkut’s University of Technology North Bangkok
  • Montean Rattanasiriwongwut King Mongkut’s University of Technology North Bangkok

DOI:

https://doi.org/10.4186/ej.2017.21.3.295

Keywords:

Photo location estimation, location uniqueness, location recognition, MapReduce structure.

Abstract

Location estimation of a photo is the method to find the location where the photo was taken that is a new branch of image retrieval. Since a large number of photos are shared on the social multimedia. Some photos are without geo-tagging which can be estimated their location with the help of million geo-tagged photos from the social multimedia. Recent researches about the location estimation of a photo are available. However, most of them are neglectful to define the uniqueness of one place that is able to be totally distinguished from other places. In this paper, we design a workflow named G-sigMR (Geo-signature MapReduce) for the improvement of recognition performance. Our workflow generates the uniqueness of a location named Geo-signature which is summarized from the visual synonyms with the MapReduce structure for indexing to the large-scale dataset. In light of the validity for image retrieval, our G-sigMR was quantitatively evaluated using the standard benchmark specific for location estimation; to compare with other well-known approaches (IM2GPS, SC, CS, MSER, VSA and VCG) in term of average recognition rate. From the results, G-sigMR outperformed previous approaches.

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Author Biographies

Pakpoom Mookdarsanit
Google Scholar

Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Thailand

Montean Rattanasiriwongwut
Google Scholar

Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Thailand

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Published In
Vol 21 No 3, Jun 15, 2017
How to Cite
[1]
P. Mookdarsanit and M. Rattanasiriwongwut, “Location Estimation of a Photo: A Geo-signature MapReduce Workflow”, Eng. J., vol. 21, no. 3, pp. 295-308, Jun. 2017.
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