Similarity Retrieval Based on SOM-Based R~*-Tree

摘要

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are usually high-dimensional data. The performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increases. In this paper, we propose a SOM-based R~*-tree(SBR-Tree) as a new indexing method for high-dimensional feature vectors. The SBR-Ttree combines SOM and R~*-tree to achieve search performance more scalable to high dimensionalities. When we build an R~*-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SBR - Tree with that of an SOM and an R~*-tree using color feature vectors extracted from 40,000 images. The result show that the SOM-based R~*-tree outperforms both the SOM and R~*-tree due to the reduction of the number of nodes required to build R~*-tree and retrieval time cost.