Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status identification

Abstract

The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm (k-NN) for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space (L*a*b*, hue saturation lightness (HSL), hue saturation value (HSV)) to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample S_r was classified by the k-NN algorithm with training set T_z according to the feature vector, which was formed from number of pixels, eccentricity ratio, compactness ratio, and Euler’s numbers. Last, while the classification confidence coefficient equaled k, made S_r as one sample of pre-training set T_z′. The training set T_z increased to T_z+1 by T_z′ if T_z′ was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm had a 98.65% identification accuracy, also selected five groups of samples to enlarge the training set from T₀ to T₅ by itself.

Key words： multi-color space; k-nearest neighbor algorithm (k-NN); self-learning; surge test

Acknowledgements

The authors wish to acknowledge the financial support from the Science and Technology Plan Projects of Guangzhou, China (Grant No. 201504010037).

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