  In most practical applications of image retrieval, high-dimensional feature
vectors are required, but current multi-dimensional indexing structures lose
their efficiency with growth of dimensions. Our goal is to propose a divisive
hierarchical clustering-based multi-dimensional indexing structure which is
efficient in high-dimensional feature spaces. A projection pursuit method has
been used for finding a component of the data, which data's projections onto it
maximizes the approximation of negentropy for preparing essential information
in order to partitioning of the data space. Various tests and experimental
results on high-dimensional datasets indicate the performance of proposed
method in comparison with others.
