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

In this paper, we present a hierarchical self-organizing map applying to scaling and rotation invariant recognition of a 256 x 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 propose 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.