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Learning-Based Interactive Retrieval in Large-Scale Multimedia Collections

  • Hisham Mohamed
  • Marc von Wyl
  • Eric Bruno
  • Stéphane Marchand-Maillet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7836)

Abstract

Indexing web-scale multimedia is only possible by distributing storage and computing efforts. Existing large-scale content-based indexing services mostly do not offer interactive relevance feedback. Here, we detail the construction of our Cross-Modal Search Engine (CMSE) implementing a query-by-example search strategy with relevance feedback and distributed over a cluster of 20 Dual core machines using MPI. We present the performance gain in terms of interactivity (search time) using a part of the Image-Net collection containing more than one million images as base example.

Keywords

Query Processing Message Passing Interface Memory Model Relevance Feedback Master Node 
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 2013

Authors and Affiliations

  • Hisham Mohamed
    • 1
  • Marc von Wyl
    • 1
  • Eric Bruno
    • 2
  • Stéphane Marchand-Maillet
    • 1
  1. 1.Viper Group - Department of Computer ScienceUniversity of GenevaSwitzerland
  2. 2.Data Mining and Knowledge Discovery - Corporate R&D DivisionFirmenich SASwitzerland

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