High-dimensional descriptor indexing for large multimedia databases
Proceedings of the 17th ACM conference on Information and knowledge management
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Videntifier™ forensic: robust and efficient detection of illegal multimedia
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Advanced Techniques in CBIR: Local Descriptors, Visual Dictionaries and Bags of Features
SIBGRAPI-TUTORIALS '09 Proceedings of the 2009 Tutorials of the XXII Brazilian Symposium on Computer Graphics and Image Processing
GPU acceleration of Eff2 descriptors using CUDA
Proceedings of the international conference on Multimedia
Understanding the security and robustness of SIFT
Proceedings of the international conference on Multimedia
Videntifier" Forensic: large-scale video identification in practice
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
Deluding image recognition in sift-based cbir systems
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
NV-Tree: nearest neighbors at the billion scale
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Computer Vision and Image Understanding
Impact of storage technology on the efficiency of cluster-based high-dimensional index creation
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
Security-oriented picture-in-picture visual modifications
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Indexing and searching 100M images with map-reduce
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Journal of Visual Communication and Image Representation
Hi-index | 0.14 |
Over the last two decades, much research effort has been spent on nearest neighbor search in high-dimensional data sets. Most of the approaches published thus far have, however, only been tested on rather small collections. When large collections have been considered, high-performance environments have been used, in particular systems with a large main memory. Accessing data on disk has largely been avoided because disk operations are considered to be too slow. It has been shown, however, that using large amounts of memory is generally not an economic choice. Therefore, we propose the NV-tree, which is a very efficient disk-based data structure that can give good approximate answers to nearest neighbor queries with a single disk operation, even for very large collections of high-dimensional data. Using a single NV-tree, the returned results have high recall but contain a number of false positives. By combining two or three NV-trees, most of those false positives can be avoided while retaining the high recall. Finally, we compare the NV-tree to Locality Sensitive Hashing, a popular method for \epsilon-distance search. We show that they return results of similar quality, but the NV-tree uses many fewer disk reads.