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Mining Regional Representative Photos from Consumer-Generated Geotagged Photos

  • Keiji Yanai
  • Qiu Bingyu
Chapter

Abstract

In this chapter, we treat with the problem of selecting representative photographs corresponding to a given keyword for regions in the worldwide dimensions. Selecting and generating such representative photographs for representative regions from large-scale collections would help us understand about local specific objects and scenes with a worldwide perspective. We propose a solution to this problem using a large-scale collection of geotagged photographs. Our method firstly extracts the most relevant images by clustering and evaluation on the visual features. Then, based on geographic information of the images, representative regions are automatically detected. Finally, we select and generate a set of representative images for the representative regions by employing the Probabilistic Latent Semantic Analysis (PLSA) modelling. The results show the ability of our approach to mine regional representative photographs, and helps us understand how objects, scenes or events corresponding to the same given keywords are visually different and discover cultural differences depending on local regions over the world.

Keywords

Visual Word Scale Invariant Feature Transform Representative Region Probabilistic Latent Semantic Analysis Representative Photo 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of Electro-CommunicationsTokyoJapan

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