An Evaluation of Intrinsic Dimensionality Estimators
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the 'Dimensionality Curse' and the 'Self-Similarity Blessing'
IEEE Transactions on Knowledge and Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Pivot selection techniques for proximity searching in metric spaces
Pattern Recognition Letters
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Approximate similarity search in metric spaces using inverted files
Proceedings of the 3rd international conference on Scalable information systems
Geodesic entropic graphs for dimension and entropy estimation in manifold learning
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
In order to achieve large scalability, indexing structures are usually distributed to incorporate more of expensive main memory during the query processing. In this paper, an indexing structure, that does not suffer from a performance degradation by its transition from main memory storage to hard drive, is proposed. The high efficiency of the index is achieved using a very effective pruning based on precomputed distances and so called locality phenomenon which substantially diminishes the number of retrieved candidates. The trade-offs for the large scalability are, firstly, the approximation and, secondly, longer query times, yet both are still bearable enough for recent multimedia content-based search systems, proved by an evaluation using visual and audio data and both metric and semi-metric distance functions. The tuning of the index's parameters based on the analysis of the particular's data intrinsic dimensionality is also discussed.