Implementation and Performance Evaluation of SOM-based R*-Tree

D.-Y. Lee, C.-S. Cheung, and S.-H. Bae (Korea)


Content-based image retrieval, self organizing map, indexing technology, wavelet, image database


Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (eg, documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(eg, R-Tree family, K-D-B tree, grid file, TV-tree) 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 mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (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. A best-matching-image-list.(BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. 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 SOM-based R -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.

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