Self-Organising Maps for Image Segmentation

被引：5
|
作者：
Wehrens, Ron [1 ]
机构：
[1] Radboud Univ Nijmegen, Inst Mol & Mat, NL-6525 ED Nijmegen, Netherlands
来源：
ADVANCES IN DATA ANALYSIS, DATA HANDLING AND BUSINESS INTELLIGENCE | 2010年
关键词：
Data fusion; Self-organising maps; Supervised mapping; Visualisation;
D O I：
10.1007/978-3-642-01044-6_34
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Self-organising maps (SOMs) have been applied in many different areas of science. In a typical application, large numbers of objects (thousands or more) are mapped to a two-dimensional grid of units in such a way that very similar objects end up in the same unit, and that neighbouring units are more similar than far-away units. The similarities of the individual units can be used in visualisation of the data by choosing appropriate colour schemes. Examples from image segmentation will show the usefulness of this approach. Often, additional information is available, e.g., class information, or measurements of a different nature. To take this extra information into account, we have extended the basic principle of SOMs to accommodate extra layers, one for each data modality. The closest unit is then given by a weighted sum of per-layer distances. The result is an overall better mapping, incorporating all available information. This is implemented in an R package "kohonen".
引用
收藏
页码：373 / 383
页数：11
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