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STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
ACM Computing Surveys (CSUR)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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Proceedings of the 12th annual ACM international conference on Multimedia
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast tracking of near-duplicate keyframes in broadcast domain with transitivity propagation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Proceedings of the 6th ACM international conference on Image and video retrieval
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
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Proceedings of the eighth ACM symposium on Document engineering
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ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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IEEE Transactions on Image Processing
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Indexing personal image collections: a flexible, scalable solution
IEEE Transactions on Consumer Electronics
IEEE Transactions on Image Processing
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Vote-based algorithms are very popular in tasks based on image local-descriptors, including object matching, panoramic stitching and near-duplicate detection. On this paper, we focus on the latter application, proposing a Bayesian approach, which allows giving a probabilistic interpretation to the distances between local descriptors in the feature space. That contrasts with traditional schemes, in which the distances are used to establish a simple unweighted vote count. Near-duplicate detection is demanded for a myriad of applications: metadata retrieval in cultural institutions, detection of copyright violations, duplicate elimination in storage, etc. The majority of current solutions are based either on voting algorithms, which are very precise, but expensive; or on the use of visual dictionaries, which are efficient, but less precise. Contrarily to raw-vote based systems, our scheme performs few database accesses; and contrarily to dictionary-based systems, it allows a fine control of the compromise between precision and efficiency. In our experiments, it yields 99% accuracy with less than 10 database accesses, in contrast with the hundreds needed in raw-voting schemes.