Effect of grouping in vector recognition system based on SOM
Effect of grouping in vector recognition system based on SOM
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DOI：
10.1109/ssci.2016.7850133
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
Masayoshi Ohta;Yuto Kurosaki;Hidetaka Ito;H. Hikawa
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文献类型：
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作者：
Masayoshi Ohta;Yuto Kurosaki;Hidetaka Ito;H. Hikawa
本文讨论了分组对基于自组织映射（SOM）的向量分类器的影响。SOM是一种无监督的学习神经网络，用于利用其拓扑保持性质形成聚类。因此，它被用于各种模式识别应用。在图像识别中，在困难的照明条件下，识别精度降低。本文提出了一种新的图像识别系统，采用分组方法。该系统根据亮度对矢量进行分组，并为每组分配多个矢量分类器。每个分类器的识别参数被调整为属于其组的向量。所提出的方法被施加到位置识别从车载摄像头上的移动的机器人获得的图像。通过对有无分组的识别系统进行比较，表明分组可以提高识别的准确率。
This paper discusses effect of grouping on vector classifiers that are based on self-organising map (SOM). The SOM is an unsupervised learning neural network, and is used to form clusters using its topology preserving nature. Thus it is used for various pattern recognition applications. In image recognition, recognition accuracy is degraded under difficult lighting conditions. This paper proposes a new image recognition system that employs a grouping method. The proposed system does the grouping of vectors according to their brightness, and multiple vector classifiers are assigned to every groups. Recognition parameters of each classifier are tuned for the vectors belonging to its group. The proposed method is applied to position identification from images obtained from an on-board camera on a mobile robot. Comparison between the recognition systems with and without the grouping shows that the grouping can improve recognition accuracy.