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System and method for identifying critical features in an ordered scale space within a multi-dimensional feature space

  • US 20050171948A1
  • Filed: 12/11/2002
  • Published: 08/04/2005
  • Est. Priority Date: 12/11/2002
  • Status: Abandoned Application
First Claim
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1. A system for identifying critical features in an ordered scale space within a multi-dimensional feature space, comprising:

  • a feature analyzer initially processing features, comprising;

    a feature extractor extracting the features from a plurality of data collections, each data collection characterized by a collection of features semantically-related by a grammar;

    a database manager normalizing each feature and determining frequencies of occurrence and co-occurrences for the features for each of the data collections;

    a mapper mapping the occurrence frequencies and the co-occurrence frequencies for each of the features into a set of patterns of occurrence frequencies and a set of patterns of co-occurrence frequencies with one such pattern for each data collection;

    an unsupervised classifier selecting the pattern for each data collection and calculating similarity measures between each occurrence frequency in the selected pattern;

    a scale space transformation projecting the occurrence frequencies onto a one-dimensional document signal in order of relative decreasing similarity using the similarity measures; and

    a critical feature identifier deriving wavelet and scaling coefficients from the one-dimensional document signal.

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