{'fraction_non_alphanumeric': 0.02074074074074074, 'fraction_numerical': 0.002962962962962963, 'mean_word_length': 4.982300884955753, '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}}
Scene modeling in global-local view for scene classification

Scene classification aims to automatically label an image among a set of semantic categories. The issue of scene modeling is critical to its classification performance. Inspired by recent psychology progresses on visual perception, we unify the current popular strategies into a 'gist' framework, and suggest a global-local view to model scenes. We evaluate our strategy on the 13 class scenes dataset mostly cited. The experiment results show that our method significantly outperforms the state-of-art methods. We believe it will give a fresh look at how to effectively model scene to benefit for scene analysis.