Similar-Video Retrieval via Learned Exemplars and Time-Warped Alignment

  • Teruki Horie
  • Masafumi Moriwaki
  • Ryota Yokote
  • Shota Ninomiya
  • Akihiro Shikano
  • Yasuo Matsuyama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8836)

Abstract

New learning algorithms and systems for retrieving similar videos are presented. Each query is a video itself. For each video, a set of exemplars is machine-learned by new algorithms. Two methods were tried. The first and main one is the time-bound affinity propagation. The second is the harmonic competition which approximates the first. In the similar-video retrieval, the number of exemplar frames is variable according to the length and contents of videos. Therefore, each exemplar possesses responsible frames. By considering this property, we give a novel similarity measure which contains the Levenshtein distance (L-distance) as its special case. This new measure, the M-distance, is applicable to both of global and local alignments for exemplars. Experimental results in view of precision-recall curves show creditable scores in the region of interest.

Keywords

Similar-video retrieval exemplar time-bound affinity propagation M-distance numerical label 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Teruki Horie
    • 1
  • Masafumi Moriwaki
    • 1
  • Ryota Yokote
    • 1
  • Shota Ninomiya
    • 1
  • Akihiro Shikano
    • 1
  • Yasuo Matsuyama
    • 1
  1. 1.Department of Computer Science and EngineeringWaseda UniversityTokyoJapan

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