Indexing Visual Features Using a Hybrid Neural Network

Jesse S. Jin; Henry C. Wang; Tom Gedeon

doi:10.20965/jaciii.2000.p0412

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JACIII Vol.4 No.6 pp. 412-416

doi: 10.20965/jaciii.2000.p0412

(2000)

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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

Received:

October 19, 2000

Accepted:

November 15, 2000

Published:

November 20, 2000

Keywords:

Visual indexing, Similarity search, High dimensional space, Surrogate coding

Abstract

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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Indexing Visual Features Using a Hybrid Neural Network

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

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