{'fraction_non_alphanumeric': 0.022592152199762187, 'fraction_numerical': 0.0023781212841854932, 'mean_word_length': 5.237037037037037, '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}}
Efficient Shape-Based Image Retrieval Based on Gray Relational Analysis and Association Rules

An improved shape based image retrieval strategy based on gray relational analysis and association rules is proposed. The choice of a suitable object representation and retrieval scheme is essential for efficient retrieval. In addition, a two-stage relevance feedback mechanism based on the GM(1, N) method and association rules is incorporated to improve the retrieval accuracy. The GM(1, N) method is used to build the re-query example for subsequent retrievals. The retrieval log files stored on the server are used for offline mining of association rules. The association rules mined from users' retrieval history can further reveal users' image searching behavior. The effectiveness of the proposed model is demonstrated on the FISH dataset.