Application of Semi-Supervised Clustering Algorithm Based on Color Feature to Corrosion Level Identification for Copper

A rapid identification method of copper corrosion level based on semi-supervised clustering algorithm was proposed. In this method, a larger number of images of corroded copper were segmented firstly in order to normalize their sizes. Then the influence of outliers was weakened by filter processing, and the color feature vector of the images was obtained by color quantization. The dimension of color histogram was reduced by the kernel principal component analysis (KPCA) method. Finally, the corresponding corrosion level of each image was obtained by taking the color feature vector extracted from the standard colorimetric card as the initial clustering center of semi-supervised k-means in combination with the color feature vector training model of pre-processed corrosion images. The results showed that classification results of corrosion level of copper by calculation through the algorithm corresponded well to the visual inspection results, indicating high accuracy of the method.