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Extract Knowledge About Geo-Location Using Context and Content Information of Geo-Tagged Social Media

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Web Information Systems Engineering – WISE 2015 (WISE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9418))

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Abstract

Recent years have witnessed a phenomenal growth of social sharing websites. These social sharing web sites host a large volume of geo-referenced and community contributed media resources. However, due to the size and inability of computers to understand the content of the images, it is inherently difficult to organize, browse or search these collections. The work proposed here addresses an interesting task of organizing these geo-referenced media on Flickr to generate visual and thematic summarization of specified geo-location. The major challenge is: how to use the unstructured and unrestricted community contributed media and annotations to generate knowledge? We propose the modified PLSA algorithm to generate representative sets of images and tags for given geo-location. We have used location metadata, tags associated with images, and the visual features of images to understand context and content of images. To deal with social tagging accuracy problem, we consider ternary interrelations and multiple intra-relations among user, image and tag and model the relations using HOSVD. The novel visualization scheme summarizes the geo-location with a rich display landscape and provides location description using location representative tags. Experiments are performed on geo-tagged Flickr images for various geo-locations. The experimental results have validated the proposed method.

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Author information

Authors and Affiliations

  1. Vidya Pratishthan’s COE, Baramati, Savitribai Phule Pune University, Pune, India

    Sheetal Takale

  2. Walchand College of Engineering, Sangli, India

    Prakash Kulkarni

Authors
  1. Sheetal Takale
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  2. Prakash Kulkarni
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Corresponding author

Correspondence to Sheetal Takale .

Editor information

Editors and Affiliations

  1. Tsinghua University, Bijing, China

    Jianyong Wang

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. Florida Atlantic University, Boca Raton, Florida, USA

    Dingding Wang

  4. Victoria University, Melbourne, Australia

    Hua Wang

  5. School of Computing & Information, Florida International University, Miami, Florida, USA

    Shu-Ching Chen

  6. Florida International University, Miami, Florida, USA

    Tao Li

  7. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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© 2015 Springer International Publishing Switzerland

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Takale, S., Kulkarni, P. (2015). Extract Knowledge About Geo-Location Using Context and Content Information of Geo-Tagged Social Media. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_40

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  • DOI: https://doi.org/10.1007/978-3-319-26190-4_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26189-8

  • Online ISBN: 978-3-319-26190-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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