{'fraction_non_alphanumeric': 0.0197869101978691, 'fraction_numerical': 0.0060882800608828, 'mean_word_length': 5.036697247706422, '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}}
Best view selection of 3D models based on unsupervised feature learning and discrimination ability

In this poster, an approach for best view selection of 3D models is proposed, which is based on the framework that formulates the selection as a problem of evaluating views' discrimination ability. Firstly, different views' features are extracted by unsupervised feature learning. Then classifiers are trained to evaluate each view's discrimination ability. A view with the best classifier has the best discrimination ability, and it is chosen as the best view of the 3D model. At last, experiments show that 3D models of same class have similar best views.