Chang Youn Oh, Dong Ju Im, Kun Seok Oh, Sang Hyun Bae

The KIPS Transactions:PartD, Vol. 8, No. 5, pp. 507-512, Oct. 2001
10.3745/KIPSTD.2001.8.5.507,   PDF Download:

Abstract

Feature-based similarity has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects. The performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increases. The R*-Tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R*-Tree as a new indexing method for high-dimensional feature vectors. The SOM-based R*-Tree combines SOM and R*-Tree to achieve search performance more scalable to high-dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. We experimentally compare the retrieval time cost of a SOM-based R*-Tree with that of an SOM and R*-Tree using color feature vectors extracted from 40,000 images. The results 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.

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