顔画像情報解析のためのクラスタ数適応型自己組織化マップシステム

概要

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The Self-organizing Map(SOM)is the most widely used artificial neural network algorithm in the unsupervised learning category. A clustering system is a major application of SOM. Normally, a SOM uses neurons of fixed size. However, it is hard to detemine the specific size of neurons that match the purpose of a clustering system, such as facial image clustering In this study, I propose a method that assesses the variation in the size of neurons according to the diversity of data. This method uses the Euclidean distance as an index to decide to whether insert a new neuron or eliminate a neuron. Test images of 3 and 26 patterns were inputted during the self-orgrnization process into the SOM which initially had 20 neurons. As a result, 7 and 69 neurons were obtained respectively. The proposed method created a clustering system that varied with the number of clusters. I applied the proposed method to classify facial images. I chose the infants′ facial images of 2 and 5 month old infants as test data, which had difference in complication of facial expressions. As a result, the number of clusters changed according to the variety of the infants′ facial egressions. Thus the proposed method is useful to make a clustering system for facial image analysis.
2007-12-30