The GC-tree: A high-dimensional index structure for similarity search in image databases

Abstract

With the proliferation of multimedia data, there is an increasing need to support the indexing and retrieval of high-dimensional image data. In this paper, we propose a new dynamic index structure called the GC-tree (or the grid cell tree) for efficient similarity search in image databases. The GC-tree is based on a special subspace partitioning strategy which is optimized for a clustered high-dimensional image dataset. The basic ideas are threefold: 1) we adaptively partition the data space based on a density function that identifies dense and sparse regions in a data space; 2) we concentrate the partition on the dense regions, and the objects in the sparse regions of a certain partition level are treated as if they lie within a single region; and 3) we dynamically construct an index structure that corresponds to the space partition hierarchy. The resultant index structure adapts well to the strongly clustered distribution of high-dimensional image datasets. To demonstrate the practical effectiveness of the GC-tree, we experimentally compared the GC-tree with the IQ-tree, the LPC-file, the VA-file, and the linear scan. The result of our experiments shows that the GC-tree outperforms all other methods.