{'fraction_non_alphanumeric': 0.015580736543909348, 'fraction_numerical': 0.0028328611898017, 'mean_word_length': 4.891666666666667, '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}}
Bag of Visual Words: A Soft Clustering Based Exposition

In this paper, we explain the bag of words representation from a soft computing perspective. The traditional Bag of word representation describes an image as a bag of discrete visual code words. Where histogram of the number of occurrences of these code words is used for image classification tasks. The drawback of the approach is that every visual feature in an image is assigned to single codeword, which leads to the loss of information regarding the other relevant code words that can represent the same feature. In this paper, we show how fuzzy and possibilistic codeword assignment improves the classification performance on Scene-15-dataset.