Scalable high-dimensional indexing with Hadoop - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2013

Scalable high-dimensional indexing with Hadoop

Denis Shestakov
  • Function : Author
  • PersonId : 937626
Diana Moise
  • Function : Author
  • PersonId : 867268
Gylfi Thór Gudmundsson
  • Function : Author
  • PersonId : 890913
Laurent Amsaleg


While high-dimensional search-by-similarity techniques reached their maturity and in overall provide good performance, most of them are unable to cope with very large multimedia collections. The 'big data' challenge however has to be addressed as multimedia collections have been explosively growing and will grow even faster than ever within the next few years. Luckily, computational processing power has become more available to researchers due to easier access to distributed grid infrastructures. In this paper, we show how high-dimensional indexing methods can be used on scientific grid environments and present a scalable workflow for indexing and searching over 30 billion SIFT descriptors using a cluster running Hadoop. Our findings could help other researchers and practitioners to cope with huge multimedia collections.
Fichier principal
Vignette du fichier
cbmi2013_submission_46.pdf (265.81 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00817378 , version 1 (24-04-2013)


  • HAL Id : hal-00817378 , version 1


Denis Shestakov, Diana Moise, Gylfi Thór Gudmundsson, Laurent Amsaleg. Scalable high-dimensional indexing with Hadoop. CBMI---International Workshop on Content-Based Multimedia Indexing, 2013, Veszprém, Hungary. ⟨hal-00817378⟩
486 View
368 Download


Gmail Facebook X LinkedIn More