{'fraction_non_alphanumeric': 0.02959830866807611, 'fraction_numerical': 0.013742071881606765, 'mean_word_length': 5.398648648648648, '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}}
Dimensionality reduction using multi-dimensional scaling for content-based retrieval

There has been much interest recently in image content based retrieval, with applications to digital libraries and image database accessing. One approach to this problem is to base retrieval from the database upon feature vectors which characterize the image texture. Since feature vectors are often high dimensional, multi-dimensional scaling, or non-linear principal components analysis (PCA) may be useful in reducing feature vector size, and therefore computation time. We have investigated a variant of the non-linear PCA algorithm described by Webb (see Pattern Recognition, vol.28, no.6, p.753-9, 1995) and its usefulness in the database retrieval problem. The results are quite impressive; in an experiment using an aerial photo database, the feature vector length was reduced by a factor of 10 without significantly reducing the retrieval performance.