{'fraction_non_alphanumeric': 0.028953229398663696, 'fraction_numerical': 0.0, 'mean_word_length': 5.659259259259259, '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}}
Data sampling for semi-supervised learning in vision-based concrete defect recognition

Appropriate selection of data for semi-supervised learning techniques can be of great significance for the attained efficacy of related approaches in pattern recognition problems. In this paper, we scrutinize the effectiveness of different data sampling approaches as labelled data generators to be used in semi-supervised learning. We explore a variety of sampling approaches that are based on the Kennard-Stone algorithm, sparse representative modeling selection, OPTICS algorithm, k-means centroids, and random selection. These approaches are explored in the context of three semi-supervised learning techniques, i.e. graph-based approaches (harmonic functions, anchor graph) and low-density separation, and evaluated in a real-world scenario of defect detection and recognition on concrete tunnel surfaces.