{'fraction_non_alphanumeric': 0.01752021563342318, 'fraction_numerical': 0.0, 'mean_word_length': 5.243697478991597, '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}}
Generic object detection in maritime environment using self-resemblance

We propose a novel unified framework for the initial detection of possible targets within the aerial images using saliency detection. Our method is a bottom-up approach and computes Locally Adaptive Regression Kernel (LARK) from the given image, which measures the likeness of a pixel to its surroundings. Visual saliency is then computed using the self-resemblance measure. The framework results in a saliency map and each pixel indicates the statistical likelihood of saliency of a feature matrix given its surrounding feature matrices. As a similarity measure, matrix cosine similarity is employed. State of the art performance is demonstrated on real aerial images.