Shape indexing using self-organizing maps

In this paper, we propose a novel approach to generating topology preserving mapping of structural shapes using the self-organizing maps (SOM). The structural information of the geometrical shapes is captured by the relational vectors. These relational attribute vectors are quantised using an SOM. Using this quantization SOM, a histogram is generated for every shape. These histograms are treated as inputs to train another SOM which yields a topology preserving mapping of the geometric shapes. By appropriately choosing the relational vectors, it is possible to generate the mapping invariant to some chosen transformations such as rotation, translation, scale, affine, or perspective. Experimental results using trademark objects are presented to demonstrate the performance of the proposed methodology.