JACIII Vol.4 No.6 pp. 412-416
doi: 10.20965/jaciii.2000.p0412


Indexing Visual Features Using a Hybrid Neural Network

Jesse S. Jin*, Henry C. Wang* and Tom Gedeon**

*Department of Computer Science, University of Sydney NSW 2006, Australia

**School of Information Technology, Murdoch University WA, Australia

October 19, 2000
November 15, 2000
November 20, 2000
Visual indexing, Similarity search, High dimensional space, Surrogate coding
Indexing and retrieving visual information is an important issue in multimedia development. It involves handling high dimensional vectors. Current tree-based high dimensional index structures, such as R-tree, SS+-tree, TV-tree, etc, have the similar low bound to the one-dimensional comparison-based search methods. It is far from being practical in the multimedia area. We propose a fast indexing method using surrogate coding. It possesses many good properties such as preserving similarity ranking and being fast in retrieval. It also preserves a clustered space and is easy to maintain.
Cite this article as:
J. Jin, H. Wang, and T. Gedeon, “Indexing Visual Features Using a Hybrid Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.4 No.6, pp. 412-416, 2000.
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Last updated on May. 28, 2024