Bayesian approach for near-duplicate image detection

Computer Science – Computer Vision and Pattern Recognition

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

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.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Bayesian approach for near-duplicate image detection does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Bayesian approach for near-duplicate image detection, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Bayesian approach for near-duplicate image detection will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-545111

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.