Combining structural and statistical features in a machine learning technique for texture classification

ABSTRACT

This paper presents a method for applying inductive learning techniques to texture description. Local texture features described as eight attributes have been extracted for each pixel from small windows (5x5, 7x7 or 9x9) centered around the pixel. The extra ninth attribute is computed from larger global area (25*25) as a co-occurrence matrix parameter. All nine attributes from an event, which is essentially a point in a 9-dimensional attribute space. Sets of such events are computed for different texture classes, and the inductive learning AQ algorithm is used to generate a given class description. Such learned descriptions are evaluated against different texture samples. Results of experiments performed on eight textural images are presented.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Induction

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Statistical computing

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding
            Subjects: Modeling and recovery of physical attributes; Texture
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.7 Feature Measurement
          Subjects: Texture

General Terms:
Algorithms, Experimentation