Texture Classification

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Discovery of Human-Competitive Image Texture
Feature Extraction Programs Using Genetic
Programming
By Brian Lam and Vic
Ciesielski
blam,vc_at_cs.rmit.edu.au RMIT
University School of Computer Science and
Information Technology
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What is texture ? Texture can be considered to be
repeating patterns of local variation of pixel
intensities.
Brodatz Textures
Vistex Textures
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Human Invented Algorithms

• Texture feature extraction algorithms can be
  grouped as follows
• Statistical
• Geometrical
• Model based
• Signal Processing

Tuceryan and Jain, Texture Analysis in The
Handbook of Pattern Recognition and Computer
Vision, World Scientific, 2nd edn., 1998
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Statistical Methods

• Local features
• Autoregressive
• Galloway run length matrix
• Haralick co-occurrence matrix
• Unser
• Sun and Wee
• Amadasun
• Dapeng
• Amalung

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Local Features

• Grey level of central pixels
• Average of grey levels in window
• Median
• Standard deviation of grey levels
• Difference of maximum and minimum grey levels
• Difference between average grey level in small
  and large windows
• Sobel feature
• Kirsch feature
• Derivative in x window
• Derivative in y window
• Diagonal derivatives
• Combine features

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Haralick Features

• First transform pixels into a co-occurrence
  matrix then calculate a (large) number of
  statistical features from the matrix.

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Geometric Methods

• Chens geometric features
• First threshold images into binary images of n
  grey levels
• Then calculate statistical features of connected
  areas.

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Model Based Methods

• These involve building mathematical models to
  describe textures.
• Markov random fields
• Fractals 1
• Fractals 2

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Signal Processing Methods

• These methods involve transforming original
  images using filters and calculating the energy
  of the transformed images.
• Laws masks
• Laines Daubechies wavelets
• Fourier transform
• Gabor filters

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Research Questions

• How do we use GP to evolve texture feature
  extraction programs ?
• - Inputs
• - Functions
• - Fitness evaluation
• Can GP generate human competitive feature
  extraction programs ?

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Texture Classification
Our Approach
Classical Approach
Feature Extraction invented by human
Feature Extraction discovered by GP
Extract Features from Vistex
Extract Features from Vistex
Training data
Testing Data
Training data
Testing Data
Classifier
Classifier
Test on testing data
Test on testing data
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Discovering Programs Using GP
Evolve feature extraction programs
Learning Data (Brodatz)
Extract Features
Evaluate Fitness
Feature Extraction programs discovered by GP
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Data Set Definitions

• Learning set 13 Brodatz textures used to evolve
  78 programs (80 of 64 x 64 images in each).
• Training set 15 Vistex textures used to train
  classifier (32 of 64 x 64 images in each ).
• Testing set 15 Vistex textures used to test
  classifier (64 of 64 x 64 images in each).

Wagner T, Texture Analysis in Handbook of
Computer Vision and Applications, Academic Press,
1999
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GP Configuration
Brodatz Texture Images
256 inputs Histogram Values Image size 64 x 64
GP System Operator plus Fitness Evaluation
Overlap between clusters
Texture Feature Extraction Programs
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Feature Space for Two Textures
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Histograms of Class 1 and Class 2 Learning Set
Textures
Evolved program X109 2X116 2X117 X126
2X132 X133 2X143 X151 X206 X238
3X242 X254
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Results
Accuracy
GP features
Wagner T, Texture Analysis in Handbook of
Computer Vision and Applications, Academic Press,
1999
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RESULTS 2

• Industrial inspection problem
• Classification of Malt Images
• Our GP features slightly more accurate than
  Haralick features

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Conclusions

• GP can generate feature extraction algorithms
  that are competitive with human developed
  algorithms.
• Evolved programs are fast compared with some of
  the human derived ones.

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Inputs
Histograms
Pixels
64
16
16
64
16 x 16 256 inputs
256 grey levels 256 inputs
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Other GP Parameters
Generation 200 Mutation rate 0.28 Cross-over
rate 0.78 Elitism 0.02