{'fraction_non_alphanumeric': 0.02122641509433962, 'fraction_numerical': 0.0023584905660377358, 'mean_word_length': 5.064285714285714, '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}}
Feature Co-occurrence Maps: Appearance-based localisation throughout the day

In this paper we present a new method, Feature Co-occurrence Maps, for appearance-based localisation over the course of a day. We show that by quantising local features in both feature and image space, discriminative statistics can be learned on the co-occurrences of features at different times of the day. This allows for matching at any time, without requiring individual images to be stored representing each time of day, and matching is performed efficiently by simultaneously matching to the entire database. We further show how matching along image sequences can be incorporated into the system and adapt existing methods by allowing for non-zero acceleration. Results on a 20km outdoor dataset show improved performance in precision-recall over state of the art.