Structural indexing of line pictures with feature generation models

  • Hirobumi Nishida
Shape Repsentation and Image Segmentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

Abstract

Structural indexing is a potential approach to efficient classification and retrieval of image patterns with respect to a very large number of models. Essential problems caused by mapping image features to discrete indices are that the indexing is sensitive to noise, scales of observation, and local shape deformations, and that a priori knowledge or feature distributions of corrupted instances are not available for each class when a large number of training data are not presented. To cope with these problems, shape feature generation techniques are incorporated into structural indexing. The feature transformation rules obtained by an analysis of some particular types of shape deformations are exploited to generate features that can be extracted from deformed patterns. The generated features are used in model database organization and classification. Experimental trials with a large number of sample data show that the shape feature generation significantly improves the classification accuracy and efficiency.

Keywords

Structural Indexing Input Pattern Characteristic Number Closed Contour Shape Deformation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Hirobumi Nishida
    • 1
  1. 1.Software Research CenterBunkyo-ku, TokyoJapan

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