  In this paper we propose a bayesian approach for near-duplicate image
detection, and investigate how different probabilistic models affect the
performance obtained. The task of identifying an image whose metadata are
missing is often demanded for a myriad of applications: metadata retrieval in
cultural institutions, detection of copyright violations, investigation of
latent cross-links in archives and libraries, duplicate elimination in storage
management, etc. The majority of current solutions are based either on voting
algorithms, which are very precise, but expensive; either on the use of visual
dictionaries, which are efficient, but less precise. Our approach, uses local
descriptors in a novel way, which by a careful application of decision theory,
allows a very fine control of the compromise between precision and efficiency.
In addition, the method attains a great compromise between those two axes, with
more than 99% accuracy with less than 10 database operations.
