Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Dimensionality reduction and similarity computation by inner product approximations
Proceedings of the ninth international conference on Information and knowledge management
ACM Computing Surveys (CSUR)
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
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
Efficient User-Adaptable Similarity Search in Large Multimedia Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Dimensionality reduction using magnitude and shape approximations
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Generating High Dimensional Data and Query Sets
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
A Telematics Service System Based on the Linux Cluster
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Multimedia Tools and Applications
Dimensionality reduction in high-dimensional space for multimedia information retrieval
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
A comparative study of dimensionality reduction techniques to enhance trace clustering performances
Expert Systems with Applications: An International Journal
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It is crucial to compute the Euclidean distance between two vectors efficiently in high-dimensional space for multimedia information retrieval. We propose an effective method for approximating the Euclidean distance between two high-dimensional vectors. For this approximation, a previous method, which simply employs norms of two vectors, has been proposed. This method, however, ignores the angle between two vectors in approximation, and thus suffers from large approximation errors. Our method introduces an additional vector called a reference vector for estimating the angle between the two vectors, and approximates the Euclidean distance accurately by using the estimated angle. This makes the approximation errors reduced significantly compared with the previous method. Also, we formally prove that the value approximated by our method is always smaller than the actual Euclidean distance. This implies that our method does not incur any false dismissal in multimedia information retrieval. Finally, we verify the superiority of the proposed method via performance evaluation with extensive experiments.