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Content Based Detection of Popular Images in Large Image Databases

  • Martin Solli
  • Reiner Lenz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

We investigate the use of standard image descriptors and a supervised learning algorithm for estimating the popularity of images. The intended application is in large scale image search engines, where the proposed approach can enhance the user experience by improving the sorting of images in a retrieval result. Classification methods are trained and evaluated on real-world user statistics recorded by a major image search engine. The conclusion is that for many image categories, the combination of supervised learning algorithms and standard image descriptors results in useful popularity predictions.

Keywords

Support Vector Machine Image Retrieval Image Category Scale Invariant Feature Transform Retrieval Result 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Martin Solli
    • 1
  • Reiner Lenz
    • 1
  1. 1.Media and Information Technology (MIT), Department of Science and Technology (ITN)Linköping UniversityNorrköpingSweden

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