The vector approximation file (VA-file) approach is an efficient high-dimensional indexing method using compression technique to overcome the difficulty of `curse of dimensionality''. The VA-file method combined with tree-based index structure can improve the querying efficiency, but it still succumbs to the `curse of dimensionality''. In this paper, a new high-dimensional indexing structure called PCR-tree for non-uniform distributed data sets was presented, which employs R-tree to manage the approximate vectors in the reduced- dimensionality space. The approximate vectors can be built in the KL transform domain, and low dimensional MBRs (minimum bounding rectangles) can be used to manage the approximations on the first few principal components. When performing k -nearest neighbor search, a lower-bound filtering algorithm is used to reject the improper nodes of PCR-tree, which can reduce the computational complexity and I/O cost without any dismissals. The experiment results on large image databases show that the new approach provides a faster search speed than other tree-structured vector approximation approaches.
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