{'fraction_non_alphanumeric': 0.012437810945273632, 'fraction_numerical': 0.0037313432835820895, 'mean_word_length': 5.491935483870968, '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}}
Clothing Color Collocation with Deep Neural Networks

Color is the foundation of well-put-together ensembles in clothing collocation. The way people coordinate and match different hues within looks may bring different visual feelings. However, clothing color collocation is an extremely challenging task due to great changes of public tastes and differences of consciousness towards color collocation. We propose a clothing color collocation model that consists of two deep neural networks based on color histograms calculated from a quantized color space and design features from clothing images. Given a clothing collocation, the model produces a score that indicates the degree that these clothes match. Experiments on Polyvore409k data sets validate the proposed model for clothing color collocation.