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Multi-modal Similarity Retrieval with Distributed Key-value Store

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Abstract

We propose a system architecture for large-scale similarity search in various types of digital data. The architecture combines contemporary highly-scalable distributed data stores with recent efficient similarity indexes and also with other types of search indexes. The system enables various types of data access by distance-based similarity queries, standard term and attribute queries, and advanced queries combining several search aspects (modalities). The first part of this work describes the generic architecture and similarity index PPP-Codes, which is suitable for our system. In the second part, we describe two specific instances of this architecture that manage two large collections of digital images and provide content-based visual search, keyword search, attribute-based access, and their combinations. The first collection is the CoPhIR benchmark with 106 million images accessed by MPEG7 visual descriptors and the second collection contains 20 million images with complex features obtained from deep convolutional neural network.

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Notes

  1. http://basho.com/riak/

  2. http://redis.io

  3. http://www.project-voldemort.com

  4. http://www.jboss.org/infinispan/

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  6. http://couchdb.apache.org

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Acknowledgements

This work was supported by the Czech Research Foundation project P103/12/G084.

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  1. Masaryk University, Brno, Czech Republic

    David Novak

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  1. David Novak
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Novak, D. Multi-modal Similarity Retrieval with Distributed Key-value Store. Mobile Netw Appl 20, 521–532 (2015). https://doi.org/10.1007/s11036-014-0561-4

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