SOM-Based Vector Recognition with Pre-Grouping Functionality

Yuto KUROSAKI; Masayoshi OHTA; Hidetaka ITO; Hiroomi HIKAWA

doi:10.1587/transinf.2017EDP7198

SOM-Based Vector Recognition with Pre-Grouping Functionality

Yuto KUROSAKI, Masayoshi OHTA, Hidetaka ITO, Hiroomi HIKAWA

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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.

Publication: IEICE TRANSACTIONS on Information 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

Authors

Yuto KUROSAKI
  Kansai University
Masayoshi OHTA
  Kansai University
Hidetaka ITO
  Kansai University
Hiroomi HIKAWA
  Kansai University

Keyword

SOM, vector recognition, position identification, parallel classifiers

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Yuto KUROSAKI, Masayoshi OHTA, Hidetaka ITO, Hiroomi HIKAWA, "SOM-Based Vector Recognition with Pre-Grouping Functionality" in IEICE TRANSACTIONS on Information, vol. E101-D, no. 6, pp. 1657-1665, June 2018, doi: 10.1587/transinf.2017EDP7198.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7198/_p

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@ARTICLE{e101-d_6_1657,
author={Yuto KUROSAKI, Masayoshi OHTA, Hidetaka ITO, Hiroomi HIKAWA, },
journal={IEICE TRANSACTIONS on Information},
title={SOM-Based Vector Recognition with Pre-Grouping Functionality},
year={2018},
volume={E101-D},
number={6},
pages={1657-1665},
abstract={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.},
keywords={},
doi={10.1587/transinf.2017EDP7198},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - SOM-Based Vector Recognition with Pre-Grouping Functionality
T2 - IEICE TRANSACTIONS on Information
SP - 1657
EP - 1665
AU - Yuto KUROSAKI
AU - Masayoshi OHTA
AU - Hidetaka ITO
AU - Hiroomi HIKAWA
PY - 2018
DO - 10.1587/transinf.2017EDP7198
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2018
AB - 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.
ER -