A divisive hierarchical clustering-based method for indexing image information
This addresses indexing efficiency for image retrieval applications, but appears incremental as it builds on existing clustering and projection techniques.
The paper tackles the problem of inefficient multi-dimensional indexing structures for high-dimensional image feature vectors by proposing a divisive hierarchical clustering-based method, achieving improved performance in tests on high-dimensional datasets.
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.