Interactive-Time Similarity Search for Large Image Collections Using Parallel VA-Files
In digital libraries, nearest-neighbor search (NN-search) plays a key role for content-based retrieval over multimedia objects. However, performance of existing NN-search techniques is not satisfactory with large collections and with high-dimensional representations of the objects. To obtain response times that are interactive, we pursue the following approach: it uses a linear algorithm that works with approximations of the vectors and parallelizes it. In more detail, we parallelize NN-search based on the VA-File in a Network of Workstations (NOW). This approach reduces search time to a reasonable level for large collections. The best speedup we have observed is by almost 30 for a NOW with only three components with 900 MB of feature data. But this requires a number of design decisions, in particular when taking load dynamism and heterogeneity of components into account. Our contribution is to address these design issues.
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