Abstract
In this paper, we propose a novel topology preserving mapping scheme called SHAPESOM to map structural shapes in a topology preserving manner. The structural information contained in geometrical structures is extracted using the pairwise geometric histograms. These histograms are quantised using a self-organising maps (SOM), as the SOMs offer a number of advantages over the standard equidistance histogram quantisation Using this trained SOM, a global pairwise histogram is generated for every structural shape. These histograms are treated as input vectors to another SOM called the SHAPESOM. As these global histograms capture the shape properties of the objects, the SOM trained using these histograms naturally generates a topology conserving mapping for the structural shapes. The topological mapping can be made to invariant to some chosen transformations such as the translation, rotation, scale and affine. This scheme can be used to organise and index large structural databases for shape based image retrieval or hypotheses generation in object recognition systems.
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© 2001 Springer-Verlag London Limited
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Suganthan, P.N. (2001). Shapesom. In: Advances in Self-Organising Maps. Springer, London. https://doi.org/10.1007/978-1-4471-0715-6_16
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DOI: https://doi.org/10.1007/978-1-4471-0715-6_16
Publisher Name: Springer, London
Print ISBN: 978-1-85233-511-3
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