{'fraction_non_alphanumeric': 0.019474196689386564, 'fraction_numerical': 0.0, 'mean_word_length': 5.506329113924051, '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}}
Information-theoretic approach to image description and interpretation

We present an information-theoretic approach to the image interpretation problems. In the context of this approach such tasks as contour extracting, constructing the most informative image features and image matching are described as a single unified problem. Our approach is based primarily on the interpretation of the image (or image set) representation problem as a Minimum Description Length (MDL) problem. The image matching turns out to be a generally adopted method of images alignment by maximization of their mutual information. However, instead of using the pixels intensities themselves a more condensed data representation form can be used to reduce the dimensionality of input data and to extract the invariant information: hierarchical image structural description. Though we developed and successfully applied the information-theoretic approach for the images matching, it can be extended to the other problems, e.g. the changes detection.