Study of the Feature Optimization Method for Electronic Nose Based on Color Space Fusion
Aimed at improving the gas discrimination capability of an optical electronic nose,seven kinds of gases are tested and classified.The response images of the sensitive arrays measured in RGB color space are transformed to other six typical color spaces.From the new seven color spaces,a L-R search algorithm based on an euclidean separability criterion is performed to pick out 18 optimal color channels to make a fusional color spaces.A joint analysis of principle component analysis(PCA)and the euclidean separability criterion comparison shows that the feature vectors upon the seven kinds of gases are much discriminable in the fusional color space than in other color spaces.
colorfeature optimizationspace fusionsearch algorithmelectronic nose
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