Parallel database systems: the future of high performance database systems
Communications of the ACM
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Image Databases: Search and Retrieval of Digital Imagery
Image Databases: Search and Retrieval of Digital Imagery
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Retrieval of Multispectral Satellite Imagery on Cluster Architectures (Research Note)
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Master-Client R-Trees: A New Parallel R-Tree Architecture
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Fast Approximate Search Algorithm for Nearest Neighbor Queries in High Dimensions
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A hyperplane based indexing technique for high-dimensional data
Information Sciences: an International Journal
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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.