Shape Indexing Using Relational Vectors and Neural Networks

摘要：

In this paper, we propose a novel approach to generating topology preserving mapping of structural shapes using the self-organising 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 quatisation 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. These SOMs may be organised into a tree-structure so that during the application phase the histogram of the query shape and the shapes most similar to the query shape can be retrieved efficiently.

作者: P. N. Suganthan

作者单位: Block S2, School of Electrical and Electronic Engineering Nanyang Technological University, Republic of Singapore 639798

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 443-448

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）