Ranking canonical views for tourist attractions

  • Authors:

  • Lin Yang;John Johnstone;Chengcui Zhang

  • Affiliations:

  • Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, USA;Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, USA;Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, USA

  • Venue:
  • Multimedia Tools and Applications
  • Year:
  • 2010

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Abstract

Online photo collections have become truly gigantic. Photo sharing sites such as Flickr ( http://www.flickr.com/ ) host billions of photographs, a large portion of which are contributed by tourists. In this paper, we leverage online photo collections to automatically rank canonical views for tourist attractions. Ideal canonical views for a tourist attraction should both be representative of the site and exhibit a diverse set of views (Kennedy and Naaman, International Conference on World Wide Web 297---306, 2008). In order to meet both goals, we rank canonical views in two stages. During the first stage, we use visual features to encode the content of photographs and infer the popularity of each photograph. During the second stage, we rank photographs using a suppression scheme to keep popular views top-ranked while demoting duplicate views. After a ranking is generated, canonical views at various granularities can be retrieved in real-time, which advances over previous work and is a promising feature for real applications. In order to scale canonical view ranking to gigantic online photo collections, we propose to leverage geo-tags (latitudes/longitudes of the location of the scene in the photographs) to speed up the basic algorithm. We preprocess the photo collection to extract subsets of photographs that are geographically clustered (or geo-clusters), and constrain the expensive visual processing within each geo-cluster. We test the algorithm on two large Flickr data sets of Rome and the Yosemite national park, and show promising results on canonical view ranking. For quantitative analysis, we adopt two medium data sets and conduct a subjective comparison with previous work. It shows that while both algorithms are able to produce canonical views of high quality, our algorithm has the advantage of responding in real-time to canonical view retrieval at various granularities.