Abstract
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.
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Choi, K.H., Shin, M.H., Bae, S.H., Kwon, C.H., Ra, I.H. (2004). Similarity Retrieval Based on SOM-Based R*-Tree. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24688-6_33
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DOI: https://doi.org/10.1007/978-3-540-24688-6_33
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