Discovery of Human-Competitive Image Texture Feature Extraction Programs Using

Genetic Programming

By Brian Lam and Vic Ciesielski

blam,vc@cs.rmit.edu.au

RMIT University

School of Computer Science and Information Technology

Brodatz Textures

Vistex Textures

What is texture ?Texture can be considered to be repeating patterns of localvariation of pixel intensities.

Human Invented AlgorithmsTexture 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

Statistical Methods

• Local features

• Autoregressive

• Galloway – run length matrix

• Haralick – co-occurrence matrix

• Unser

• Sun and Wee

• Amadasun

• Dapeng

• Amalung

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

Haralick Features

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

Geometric Methods

Chen’s geometric features

• First threshold images into binary images of n grey levels

• Then calculate statistical features of connected areas.

Model Based Methods

These involve building mathematical models to describe textures.

• Markov random fields

• Fractals 1

• Fractals 2

Signal Processing Methods

These methods involve transforming original images using filters and calculating the energy of the transformed images.

• Law’s masks

• Laines – Daubechies wavelets

• Fourier transform

• Gabor filters

Research Questions

1. How do we use GP to evolve texture feature extraction programs ?

- Inputs

- Functions

- Fitness evaluation

2. Can GP generate human competitive feature extraction programs ?

Texture Classification

Classical Approach

Feature Extraction invented by human

Extract Features from Vistex

Training data Testing Data

ClassifierTest on testing data

Our Approach

Feature Extraction discoveredby GP

Extract Features from Vistex

Training data Testing Data

ClassifierTest on testing data

Learning Data (Brodatz)

Evolve feature extraction programs

Extract Features

Evaluate Fitness

Feature Extraction programs discovered by GP

Discovering Programs Using GP

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

Brodatz Texture Images

256 inputsHistogramValuesImage size64 x 64

GP SystemOperator : plusFitness Evaluation : Overlap between clusters

Texture Feature Extraction Programs

GP Configuration

Feature Space for Two Textures

Histograms of Class 1 and Class 2 Learning Set Textures

Evolved program :X109 + 2*X116 + 2*X117 +X126 + 2*X132 + X133 +2*X143 + X151 +X206 + X238 +3*X242 + X254

Results

0102030405060708090

Local

feat

ures

Fracta

l (2)

Fracta

l (1)

Sun &

Wee

Amad

asun

Mak

ov

Gal

loway

Mao

& Ja

in

Pikaz

& A

verb

uch

Dapen

g

GP fe

atur

es

Gab

orLain

e

Har

alick

Laws

Fourie

r coe

ff.

Unser

Amelu

ngChe

n

GP features

*Wagner T, “Texture Analysis” in Handbook of Computer Vision and Applications, Academic Press, 1999

Accuracy %

RESULTS 2

• Industrial inspection problem

• Classification of Malt Images

• Our GP features slightly more accurate than Haralick features

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.

16

16

64

64

16 x 16 = 256 inputs

0

10

20

30

40

50

60

70

80

90

1 22 43 64 85 106 127 148 169 190 211 232 253

256 grey levels = 256 inputs

Histograms

Inputs

Pixels

Generation : 200Mutation rate : 0.28Cross-over rate : 0.78Elitism : 0.02

Other GP Parameters