A Divisive Hierarchical Clustering-based Method for Indexing Image Information

It is conventional to use multi-dimensional indexing structures to accelerate search operations in content-based image retrieval systems. Many efforts have been done in order to develop multi-dimensional indexing structures so far. 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. Increase in dimensions of data space leads to exponentially growth of the search space and increase of the number of nodes in multi-dimensional indexing structure, as well as increase in overlap between nodes in multi-dimensional indexing structures. These problems lead to increase in cost of search through indexing structure and therefore to reduction in efficiency of these structures in high-dimensional spaces. The main goal of this research is to propose a divisive hierarchical clustering-based multi-dimensional indexing structure in order to manage high-dimensional feature vectors extracted from images, which also prevents overlapping in its structure.Various tests and analyses of experimental results on high-dimensional datasets indicate the performance of our proposed method in comparison with others.