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

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3 Author(s)
K. Sookhanaphibar ; Dept. of Math., Chulalongkorn Univ., Bangkok, Thailand ; K. W. Wong ; C. Lursinsap

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.

Published in:

Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on  (Volume:4 )

Date of Conference:

18-22 Nov. 2002