BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
An Algroithm for Finding Best Matches in Logarithmic Expected Time
An Algroithm for Finding Best Matches in Logarithmic Expected Time
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Content-Based Retrieval of Images for Cultural Institutions Using Local Descriptors
GMAI '06 Proceedings of the conference on Geometric Modeling and Imaging: New Trends
Geometric consistency checking for local-descriptor based document retrieval
Proceedings of the 9th ACM symposium on Document engineering
Adaptive parallel approximate similarity search for responsive multimedia retrieval
Proceedings of the 20th ACM international conference on Information and knowledge management
Bayesian approach for near-duplicate image detection
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
| Hi-index | 0.00 |
In this paper we introduce a system for the identification of visual documents. Since it stems from content-based document indexing and retrieval, our system does not need to rely on textual annotations, watermarks or other metadata, which can be missing or incorrect. Our retrieval system is based on local descriptors, which have been shown to provide accurate and robust description. Because of the high computational costs associated to the matching of local descriptors, we propose Projection KD-Forest: an indexing technique which allows efficient approximate k nearest neighbors search. Experiments demonstrate that the Projection KD-Forest allows the system to provide prompt results with negligible loss on accuracy. The Projection KD-Forest also compares well when contrasted to other strategies of k nearest neighbors search.