IDENTIFYING CRITICAL FEATURES IN ORDERED SCALE SPACE

摘要

A system (10) and method (100) for identifying critical features (211) in an ordered scale space within a multi-dimensional feature space is described. Features (173) are extracted from a plurality of data collections (76). Each data collection (76) is characterized by a collection of features (173) semantically-related by a grammar. Each feature (173) is normalized and frequencies (183) of occurrence and co-occurrences (78) for the feature (173)are determined. The occurrence frequencies (183) and the co-occurrence frequencies (78) for each of the features (173) are mapped into a set of patterns of occurrence frequencies (183) and a set of patterns of co- occurrence frequencies (79). The pattern for each data collection (76) is selected and distance measures between each occurrence frequency (183) in th e selected pattern is calculated. The occurrence frequencies (183) are project ed onto a one-dimensional document signal (81) in order of relative decreasing similarity using the similarity measures. Wavelet and scaling coefficients (81) are derived from the one-dimensional document signal using multiresolution analysis.