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Scalable high-dimensional indexing with Hadoop

Denis Shestakov 1 Diana Moise 1 Gylfi Thór Gudmundsson 1 Laurent Amsaleg 1 
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : 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.
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Submitted on : Wednesday, April 24, 2013 - 2:57:45 PM
Last modification on : Thursday, January 20, 2022 - 4:18:42 PM
Long-term archiving on: : Thursday, July 25, 2013 - 4:12:41 AM


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  • 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⟩



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