SOM-Based Vector Recognition with Pre-Grouping Functionality

Yuto KUROSAKI  Masayoshi OHTA  Hidetaka ITO  Hiroomi HIKAWA  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.6   pp.1657-1665
Publication Date: 2018/06/01
Publicized: 2018/03/20
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7198
Type of Manuscript: PAPER
Category: Biocybernetics, Neurocomputing
SOM,  vector recognition,  position identification,  parallel classifiers,  

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This paper discusses the effect of pre-grouping on vector classification based on the self-organizing map (SOM). The SOM is an unsupervised learning neural network, and is used to form clusters of vectors using its topology preserving nature. The use of SOMs for practical applications, however, may pose difficulties in achieving high recognition accuracy. For example, in image recognition, the accuracy is degraded due to the variation of lighting conditions. This paper considers the effect of pre-grouping of feature vectors on such types of applications. The proposed pre-grouping functionality is also based on the SOM and introduced into a new parallel configuration of the previously proposed SOM-Hebb classifers. The overall system is implemented and applied to position identification from images obtained in indoor and outdoor settings. The system first performs the grouping of images according to the rough representation of the brightness profile of images, and then assigns each SOM-Hebb classifier in the parallel configuration to one of the groups. Recognition parameters of each classifier are tuned for the vectors belonging to its group. Comparison between the recognition systems with and without the grouping shows that the grouping can improve recognition accuracy.