Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Time-Constrained Keyframe Selection Technique
Multimedia Tools and Applications
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast and Robust Search Method for Short Video Clips from Large Video Collection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Towards effective indexing for very large video sequence database
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A quick search method for audio and video signals based on histogram pruning
IEEE Transactions on Multimedia
Efficient video indexing scheme for content-based retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Efficient video similarity measurement with video signature
IEEE Transactions on Circuits and Systems for Video Technology
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In a video database, the similarity between video sequences is usually measured by the percentages of similar frames shared by both video sequences, where each frame is represented as a high-dimensional feature vector. The direct computation of the similarity measure involves time-consuming sequential scans over the whole dataset. On the other hand, adopting existing indexing technique to high-dimensional datasets suffers from the “Dimensionality Curse”. Thus, an efficient and effective indexing method is needed to reduce the computation cost for the similarity search. In this paper, we propose a Multi-level Hierarchical Divisive Dimensionality Reduction technique to discover correlated clusters, and develop a corresponding indexing structure to efficiently index the clusters in order to support efficient similarity search over video data. By using dimensionality reduction techniques as Principal Component Analysis, we can restore the critical information between the data points in the dataset using a reduced dimension space. Experiments show the efficiency and usefulness of this approach.