A Topographic Support Vector Machine: Classification Using Local Label Configurations

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Johannes Mohr, Klaus Obermayer


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 topo- graphical 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 to- pographic 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 Sup- port Vector Machine' (TSVM), which is based on a topographic kernel and a self-consistent solution to the label assignment shown to be equiv- alent to a recurrent neural network. The performance of the algorithm is compared to a conventional SVM on a cell image segmentation task.