Combining geometrical and textured information to perform image classification

Jean François Aujol*, Tony Chan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


In this paper, we propose a framework to carry out supervised classification of images containing both textured and non textured areas. Our approach is based on active contours. Using a decomposition algorithm inspired by the recent work of Y. Meyer, we can get two channels from the original image to classify: one containing the geometrical information, and the other the texture. Using the logic framework by Chan and Sandberg, we can then combine the information from both channels in a user definable way. Thus, we design a classification algorithm in which the different classes are characterized both from geometrical and textured features. Since natural images are combinations of both textured and non textured patterns, this integrative approach enlarges the scope of possible applications for active contours-based classification algorithms.

Original languageEnglish (US)
Pages (from-to)1004-1023
Number of pages20
JournalJournal of Visual Communication and Image Representation
Issue number5
StatePublished - Oct 1 2006


  • Active contour
  • Classification
  • Decomposition
  • Geometrical image
  • Level-set
  • Logic model
  • PDE
  • Texture
  • Wavelets

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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