Abstract
Most modern image database systems employ content-based image re trieval techniques and various multi-dimensional indexing structures to speed up the query performance. While the first aspect ensures an intuitive re trieval for the user, the latter guarantees an efficient han dling of huge data amounts. How ever, beyond a system inherent threshold only the simultaneous paral lelisa tion of the indexing structure can improve the system’s performance. In such an ap proach one of the key factors is the de-clustering of the data. To tackle the high lighted issues, this pa per proposes an effective multi-dimensional in dex strat egy with de-clustering based on the vantage point tree with suitable simi lar ity measure for content-based re trieval. The conducted experiments show the effec tive and efficient behaviour for an actual image database.
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References
Gong, Y.: Intelligent Image Databases, pp. 55–59. Kluwer Academic Publishers, Dordrecht (1998)
Castelli, V., Bergman, L.D.: Image Databases: Search and Retrieval of Digital Imagery, p. 263. John Wiley & Sons. Inc., Chichester (2002)
Wu, L., Bretschneider, T.: VP-EMD tree: An efficient indexing strategy for data with varying dimension and order. In: Proceedings of the International Conference on Imaging Science, Systems and Technology, pp. 421–426 (2004)
Pramanik, S., Li, J.: Fast approximate search algorithm for nearest neighbor queries in high dimensions. In: Proceedings of the IEEE International Conference on Data Engineering, p. 251 (1999)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision, 99–121 (2000)
DeWitt, D.J., Gray, J.: Parallel Database Systems: The future of high performance database processing. Communications of the ACM 36(6), 85–98 (1992)
Schnitzer, B., Leutenegger, S.T.: Master-Client R-trees: A new parallel R-tree architecture. In: Proceedings of the Conference on Scientific and Statistical Database Management, pp. 68–77 (1999)
Bretschneider, T., Kao, O.: Retrieval of multispectral satellite imagery on cluster architectures. In: Proceedings of the EuroPar. LNCS, pp. 342–346 (2002)
Bretschneider, T., Kao, O.: A retrieval system for remotely sensed imagery. In: Proceedings of the International Conference on Imaging Science, Systems and Technology, vol. 2, pp. 439–445 (2002)
Li, Y., Bretschneider, T.: Supervised content-based satellite image retrieval using piecewise defined signature similarities. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, vol. 2, pp. 734–736 (2003)
Chiueh, T.C.: Content-based image indexing. In: Proceedings of the International Conference on Very Large Data Bases, pp. 582–593 (1994)
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Wu, L., Bretschneider, T. (2005). An Effective Multi-dimensional Index Strategy for Cluster Architectures. In: Leow, WK., Lew, M.S., Chua, TS., Ma, WY., Chaisorn, L., Bakker, E.M. (eds) Image and Video Retrieval. CIVR 2005. Lecture Notes in Computer Science, vol 3568. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526346_20
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DOI: https://doi.org/10.1007/11526346_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27858-0
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