Efficient similarity search on multimedia databases by Mariela Lopresti, Natalia Miranda, Fabiana Piccoli, Nora Reyes.
Abstract:
Manipulating and retrieving multimedia data has received increasing attention with the advent of cloud storage facilities. The ability of querying by similarity over large data collections is mandatory to improve storage and user interfaces. But, all of them are expensive operations to solve only in CPU; thus, it is convenient to take into account High Performance Computing (HPC) techniques in their solutions. The Graphics Processing Unit (GPU) as an alternative HPC device has been increasingly used to speedup certain computing processes. This work introduces a pure GPU architecture to build the Permutation Index and to solve approximate similarity queries on multimedia databases. The empirical results of each implementation have achieved different level of speedup which are related with characteristics of GPU and the particular database used.
No images have been published, yet, in the widening scandal around David Petraeus.
When they do, searching multimedia databases such as Flickr, Facebook, YouTube and others will be a hot issue.
Once found, there is the problem of finding unique ones again and duplicates not again.