Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Multimedia Systems - Special section on video libraries
Towards effective indexing for very large video sequence database
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
Fast and robust short video clip search for copy detection
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
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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With ever more advanced development in video devices and their extensive usages, searching videos of user interests from large scale repositories, such as web video databases, is gaining its importance. However, the huge complexity of video data, caused by high dimensionality of frames (or feature dimensionality) and large number of frames (or sequence dimensionality), prevents existing content-based search engines from using large video databases. Hence, dimensionality reduction on the data turns out to be most promising. In this paper, we propose a novel video reduction method called Optimal Dual Dimensionality Reduction (ODDR) to dramatically reduce the video data complexity for accurate and quick search, by reducing the dimensionality of both feature vector and sequence. For a video sequence, ODDR first maps each high dimensional frame into a single dimensional value, followed by further reducing the sequence into a low dimensional space. As a result, ODDR approximates each long and high dimensional video sequence into a low dimensional vector. A new similarity function is also proposed to effectively measure the relevance between two video sequences in the reduced space. Our experiments demonstrate the effectiveness of ODDR and its gain on efficiency by several orders of magnitude.