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.
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Mohamed, H., von Wyl, M., Bruno, E., Marchand-Maillet, S. (2013). Learning-Based Interactive Retrieval in Large-Scale Multimedia Collections. In: Detyniecki, M., García-Serrano, A., Nürnberger, A., Stober, S. (eds) Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation. AMR 2011. Lecture Notes in Computer Science, vol 7836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37425-8_1
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DOI: https://doi.org/10.1007/978-3-642-37425-8_1
Publisher Name: Springer, Berlin, Heidelberg
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