In this paper, a neural network model, the hypercolumn model (HCM), which is applicable to general image recognition, is proposed. The HCM is a combination model of hierarchical self-organizing maps (HSOM) and neocognitron (NC); it resolves the disadvantages of both the HSOM and the NC, and inherits all of the advantages of both models. The HSOM quantizes and nonlinearly maps an input space into a small dimensional feature map. Using this feature map, the HSOM can function as an unsupervised clustering method and can easily solve pattern recognition problems, which are hard for traditional linear methods or for multilayered perceptrons using the back-propagation learning method because of the complex shapes of the class boundaries. On the other hand, the NC is a multilayered network, in which layers to extract local features and layers to integrate shifted features alternate. Using this structure, the NC can reduce the dimensionality gradually and recognize shifted and scaled patterns. By combining the characteristics of the HSOM and the structure of the NC, the HCM can be made applicable to general image recognition problems which have high dimensional input data and in which the shapes of the class boundaries are quite complex because of the high abstraction level of the classification.
|ジャーナル||Systems and Computers in Japan|
|出版ステータス||出版済み - 2月 2000|
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