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Application of hierarchical self-organizing mapping to invariant recognition of color-texture images

Sookhanaphibarn, K., Wong, K.W. and Lursinsap, C. (2002) Application of hierarchical self-organizing mapping to invariant recognition of color-texture images. In: 9th International Conference on Neural Information Processing (ICONIP '02), 18-22 November 2002, Singapore pp. 2113-2117.

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    Abstract

    In this paper, we present a hierarchical self-organizing map applying to scaling and rotation invariant recognition of a 256×256-pixel color-texture image. Since Kohonen's self-organizing mapping is not embedded with the invariant ability, some learning modifications are added in rotation and scaling invariant self-organizing map (RSISOM). The concept of hierarchy self-organizing map, furthermore, is developed to improve the performance of RSISOM for a color image recognition. In the experiment, the proposed algorithm shows the efficient invariant capability under scaling and rotation as well as the distinguish capability in different color-texture images. Furthermore, the computational time after applying the concept of Hierarchy in RSISOM approach is three times less than the computational time of the original RSISOM.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: © 2002 IEEE
    Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    URI: http://researchrepository.murdoch.edu.au/id/eprint/1016
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