International Journal of Computer Vision
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
Fast Indexing and Visualization of Metric Data Sets using Slim-Trees
IEEE Transactions on Knowledge and Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The Amsterdam Library of Object Images
International Journal of Computer Vision
A compact space decomposition for effective metric indexing
Pattern Recognition Letters
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Efficient and Flexible Cluster-and-Search for CBIR
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
A scalable re-ranking method for content-based image retrieval
Information Sciences: an International Journal
Image and Vision Computing
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Similarity search in high-dimensional metric spaces is a key operation in many applications, such as multimedia databases, image retrieval, object recognition, and others. The high dimensionality of the data requires special index structures to facilitate the search. Most of existing indexes are constructed by partitioning the data set using distance-based criteria. However, those methods either produce disjoint partitions, but ignore the distribution properties of the data; or produce non-disjoint groups, which greatly affect the search performance. In this paper, we study the performance of a new index structure, called Ball-and-Plane tree (BP-tree), which overcomes the above disadvantages. BP-tree is constructed by recursively dividing the data set into compact clusters. Distinctive from other techniques, it integrates the advantages of both disjoint and non-disjoint paradigms in order to achieve a structure of tight and low overlapping clusters, yielding significantly improved performance. Results obtained from an extensive experimental evaluation with real-world data sets show that BP-tree consistently outperforms state-of-the-art solutions.