{'fraction_non_alphanumeric': 0.021114864864864864, 'fraction_numerical': 0.0, 'mean_word_length': 5.54696132596685, '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}}
A mathematical approach towards semi-automatic image annotation

In this paper, an interactive approach to obtain semantic annotations for images is presented. The proposed approach aims at what millions of single, online and cooperative gamers are keen to do, enjoy themselves in a competitive environment. It focuses on computer gaming and the use of humans in a widely distributed fashion. This approach deviates from the conventional “content-based image retrieval (CBIR)” paradigm favoured by the research community to tackle the problems related to the semantic annotation and tagging of multimedia contents. The proposed approach uses a multifaceted mathematical model based on game theories to aggregate numbers of different key-paradigms, such as Image Processing, Machine Learning and Game based approaches to generate accurate annotations. As a consequence, this approach is capable of identifying less-rational (cheating oriented) players, thus eliminating them from generating incorrect annotations. The performance of the proposed framework is tested with a number of game players. Result shows that this approach is capable of obtaining correct annotations in practice.