A Topographic Support Vector Machine: Classification Using Local Label Configurations

The standard approach to the classification of objects is to consider the examples as independent and identically distributed (iid). In many real world settings, however, this assumption is not valid, because a topographical relationship exists between the objects. In this contribution we consider the special case of image segmentation, where the objects are pixels and where the underlying topography is a 2D regular rectangular grid. We introduce a classification method which not only uses measured vectorial feature information but also the label configuration within a topographic neighborhood. Due to the resulting dependence between the labels of neighboring pixels, a collective classification of a set of pixels becomes necessary. We propose a new method called 'Topographic Support Vector Machine' (TSVM), which is based on a topographic kernel and a self-consistent solution to the label assignment shown to be equivalent to a recurrent neural network. The performance of the ...

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Johannes Mohr, Klaus Obermayer
• Keywords: Image Segmentation, NIPS 2004, NIPS 2007, Topographic, Topographic Support Vector