{'fraction_non_alphanumeric': 0.018504240555127217, 'fraction_numerical': 0.002313030069390902, 'mean_word_length': 5.555555555555555, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An Improved GMM-Based Algorithm With Optimal Multi-Color Subspaces for Color Difference Classification of Solar Cells

Automatic color classification for solar cells is challenging because of the tiny color difference and low contrast. To address this problem, a color feature selection and classification frame is proposed in this paper. First, an intuitive multi-color space feature performance evaluation scheme is presented to select the optimal color subspaces that help to enormously enlarge the tiny color difference of solar cell images. And the optimal color subspaces can also be illustrated by employing multi-color space visualization method with combinational Mosaic images. Second, a nine-dimensional feature vector consisting of mean, variance, and skewness in the three optimal subspaces is extracted by utilizing the serial fusion technique. Third, an improved Gaussian mixture model in supervised manner for color classification is proposed by employing a k-means method based on adjacent rules, which helps to eliminate isolated points and enhances the classification performance. Finally, experimental results show that the overall performance of the proposed method achieves 97.9%, and outperform other algorithms especially for the tiny color difference of solar cell images.