[IJ] Automatic Grouping in Trained SOFM via Graph Cuts

Under Review
(Pattern Recognition Letters - SCIE)

Abstract.

A Self-Organizing Feature Map (SOFM) is an unsupervised neural network and a very powerful tool for classifying and visualizing high-dimensional data sets. However, even though SOFMs have already been applied to many engineering problems, post-processing is still required, where similar output neurons after training the SOFMs are grouped into classes, which is invariably performed in manual. Moreover, existing algorithms that automatically group the neurons of a trained SOFM, such as the k-means, do not yield satisfactory results, especially when the grouped data shows unrestricted and arbitrary shapes. This paper proposes an automatic grouping method for a trained SOFM that can deal with arbitrary shapes of grouped data using graph cuts. In previous approaches using graph cuts, the graph is manually constructed based on prior data given by users, which hinders researchers from using it for automatic system. However, a mode-seeking in a distance matrix automatically obtains the prior data, and also can analyze arbitrary-shaped classes. Experimental results demonstrated the effectiveness of the proposed method for texture segmentation, with improved precision rates when compared with conventional clustering algorithms.

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2008.