{'fraction_non_alphanumeric': 0.008739076154806492, 'fraction_numerical': 0.0, 'mean_word_length': 5.265625, '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}}
Background scene classification robust to the influence of human regions

We propose a background scene classification method robust to the influence of human regions. Conventional methods classify scene of an image by using image features extracted from entire region in the image. Therefore, in these methods, the influence of the human region such as color of the skin and the clothes reduces classification accuracy of the background scene. Our method classifies background scene of an image by using image features extracted from only background region except detected human regions. The experimental results show that the proposed method improves average of the rate at the balance point between recall rate and precision rate in almost all background scenes compared to the conventional method.