{'fraction_non_alphanumeric': 0.02154398563734291, 'fraction_numerical': 0.0, 'mean_word_length': 5.340909090909091, '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}}
Learning to associate relevant photos to georeferenced textual documents

The association of illustrative photos to textual contents is a challenging cross-media retrieval problem with many practical applications. We have, for instance, that the association of photos to specific parts of travelogues, i.e. textual descriptions for travel experiences, may lead to a better usage of these documents. Despite the huge number of high quality photos in websites like Flickr, these photos are currently not being properly explored in cross-media retrieval tasks.