SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Some approaches to best-match file searching
Communications of the ACM
Perceptual Metrics for Image Database Navigation
Perceptual Metrics for Image Database Navigation
Fast Indexing and Visualization of Metric Data Sets using Slim-Trees
IEEE Transactions on Knowledge and Data Engineering
Approximate Processing of Multiway Spatial Joins in Very Large Databases
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Contrast Plots and P-Sphere Trees: Space vs. Time in Nearest Neighbour Searches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Approximate Algorithms for Distance-Based Queries in High-Dimensional Data Spaces Using R-Trees
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
Approximate similarity retrieval with M-trees
The VLDB Journal — The International Journal on Very Large Data Bases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
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Searching for the exact answer to a similarity query is an expensive process considering computational resources, such as memory and processing time requirements. Moreover, comparison operations over multimedia data is even more expensive than over traditional data such as numbers and small character strings. Therefore, when comparing multimedia data, the comparison computations usually consider some properties extracted from the data elements. In this way, exact queries involving this kind of data return data that is exact regarding the properties compared, but not necessarily exact regarding the multimedia data itself. For example, searching for similar images regarding their colors return images whose color histogram are the most similar, but the retrieved images can be very different regarding, for instance, the shape the objects pictured. Therefore, for applications dealing with complex data types, trading exact answering with query time response can be worthwhile. In this paper we propose to use techniques based on genetic algorithms to allow retrieving data indexed in a metric access methods within a limited, user-defined, amount of time. We show that these techniques lead to much faster execution, without reducing the quality of the answer. We also present experimental evaluation using real datasets, showing that suitable results can be obtained in a fraction of the time required to obtain the exact answer.