Pattern Recognition & Detection: Texture Classifier ECE 4226 Presented By: Denis Petrusenko December...

Pattern Recognition & Detection: Texture Classifier

ECE 4226Presented By: Denis Petrusenko

December 10, 2007

2

• Introduction– Problem description– Feature extraction– Textures used– Models considered– Overview of work being presented

• Implementation Details• Experimentation• Summary• Conclusion• Demo• References

Overview

3

• Texture Recognition– Texture: “The characteristic appearance of a

surface having a tactile quality”• Problem– Given: texture samples for different classes– Objective: recognize which class a never-before-

seen texture sample belongs to

Overview: Problem Description

4

• Histogram H(k) with K=0…L-1 [1]– L gray intensity levels vs number of occurrences– Represented by L-dimensional feature vector– Several approaches for summarizing H(k)

• Intensity Moments– First, calculate mean:– Then, central moments

» » µ2 : Variance, µ3 : Skewness, µ4 : Kurtosis

• Entropy:• Uniformity:

Overview: Feature Extraction

1

0

( )L

k

m kH k

1

0

( ) ( )n

nn

k

µ k m H k

1

0

( ) log ( )L

k

E H k H k

1

2

0

( )L

k

U H k

5

• GLCM , [1]• Relationship R: pixels shifted, usually by 1• Comes out to L2 features• Again, several ways to summarize– Maximum– Square Sum– Intensity Moments

Feature Extraction: Gray Level Co-Occurrence Matrix

,( ) , , 0, 1, 2,...nn k l

k l

Q k l C k l n

,

,

( , )

( , )k l

k l

R k lC

R k l

2,sq k l

k l

C Cmax ,

,max k lk l

C C

L LC

6

• Entropy• Uniformity• Variance• Skewness• Kurtosis• GLCM Features

– Cmax– Csq– Qp-2– Qp+2

• For all calculations, L = 16

Feature Extraction: Actual Features Used

7

• Picked 8 textures from Brodatz set [10]• Cut out a 200x200 chunk for speed• Samples were taken with a sliding window– Window size 60x60 pixels• Keeping it close to example size for consistency

– Arbitrary overlap of 15 pixels• 3 to 4 random samples per class for testing– Taken before resizing to allow for fresh data

Introduction: Textures Used

8

Introduction: Textures Used

9

• Parzen Windows Classifier (PWC) [2]– Basically, find least error• Training data versus arbitrary example

– Uses class label from closest training data– Training is just computing priors• All the “hard work” happens during classification

Introduction : Models Considered

10

• Multi-Level Perceptron (MLP) [3]

Introduction : Models Considered

11

• Created working texture recognizer– Training images broken down into chunks

• Example-sized window scans across with overlap• Dimensions controlled from GUI

– Feature extraction pulls 9 unique features• Calculates means and st. dev. for normalization• Arbitrary samples are normalized prior to classification

– Training data passed to classifiers• PWC just uses the training data directly• MLP runs training until some error threshold

– Arbitrary samples classified correctly most of the time

Introduction : Overview of Work

12

• Each classifier (PWC, MLP) is a class– Abstract class takes care of generic stuff• Keeping track of training data• Static feature extraction code

– Histogram intensity moments– GLCM calculations

• Examples in vector form, with class label• Vectors can do most basic vector operations• Used C# and Windows Forms for UI

Implementation Details

13

• PWC uses a Gaussian as the kernel function– Spread was set to 0.5 and 100,000– Does not make any difference after normalization

• MLP– Maximum error rate 8%– Class count * 2 hidden nodes– Learning rate set to 0.20– Everything is adjustable from the GUI– Uses backpropagation [4] for updating weights

13


1414

• Training data cached when training starts• Don’t have to load features every time• Classifier states not cached– PWC only has priors for state• Negligible time to compute them every time

– MLP produces different results every run• Due to random weights assignment

• Training progress shown in real time

14


15

• Histogram: Entropy

Experimentation: Linear Feature Separation

1.75

1.95

2.15

2.35

2.55

2.75

2.95

3.15

3.35

3.55

3.75

12345678

16

• Histogram: Uniformity

0.07

0.12

0.17

0.22

0.27

0.32

0.37

0.42

12345678


17

• Histogram: Variance

17


0

5

10

15

20

25

30

12345678

1818

• Histogram: Skewness

18


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

-140

-90

-40

10

60

110

12345678

1919

• Histogram: Kurtosis

19


0

200

400

600

800

1000

1200

1400

12345678

202020

• GLCM: Cmax

20


0.02

0.12

0.22

0.32

0.42

0.52

12345678

212121

• GLCM: Csq

21


-0.0499999999999997

2.91433543964104E-16

0.0500000000000003

0.1

0.15

0.2

0.25

0.3

12345678

222222

• GLCM: Qn +2

22


0

2

4

6

8

10

12

14

16

12345678

232323

• GLCM: Qn -2

23


0.17

0.22

0.27

0.32

0.37

0.42

0.47

0.52

12345678

24

3 0 0 0 0 0 0 00 3 0 0 0 0 0 00 0 3 0 0 0 0 00 0 0 3 0 0 0 00 0 0 0 3 0 0 00 0 0 0 0 3 0 00 0 0 0 1 0 3 00 0 0 0 0 0 0 3

Experimentation: Confusion Matrix

25

• 0.080 – Default, 1 error• 0.196 – Same as default• 0.260 – 22 errors• 1.851 – 22 errors– Total failure

Experimentation: MLP Min Error

0 0 0 0 0 3 0 00 0 0 0 0 3 0 00 0 0 0 0 3 0 00 0 0 0 0 3 0 00 0 0 0 0 3 0 00 0 0 0 0 3 0 00 0 0 0 0 4 0 00 0 0 0 0 3 0 0

0 0 0 0 0 0 0 30 0 0 0 0 2 0 10 0 0 0 0 0 0 30 0 0 0 0 0 0 30 0 0 0 0 0 0 30 0 0 0 0 0 0 30 0 0 0 0 0 0 40 0 0 0 0 0 0 3

26

Experimentation: MLP Hidden Nodes

6 Nodes:3 . . . . . . .. 3 . . . . . .. . 3 . . . . .. . . 2 1 . . .. . . . 3 . . .. . . . . 3 . .. . . . 1 . 3 .. . . . . . . 3

5 Nodes:3 . . . . . . .. 3 . . . . . .. . 3 . . . . .. . . 3 . . . .. . . . 3 . . .. . . . . 2 . 1. . . . 1 . 3 .. . . . . . . 3

4 Nodes:3 . . . . . . .. 3 . . . . . .. . 3 . . . . .. . . 2 1 . . .. . . . 3 . . .. . . . . 2 . 1. 1 . . . . 3 .. . . . . . . 3

3 Nodes:3 . . . . . . .. 3 . . . . . .. . 2 . . . 1 .. . . . 3 . . .. . . . 3 . . .. . . . . 2 . 1. 2 . . . . 2 .. . . . . . . 3

2 Nodes:. . . . . . 3 .. 3 . . . . . .. . . 1 . 2 . .. . . 1 1 . 1 .. . . . 3 . . .. . . . . . . 3. 1 . . 1 . 2 .. . . . . . . 3

1 Node:. . . . . . . 3. . . . . . . 3. . . . . . . 3. . . . . . . 3. . . . . . . 3. . . . . . . 3. . . . . . . 4. . . . . . . 3

7 Nodes:3 . . . . . . .. 3 . . . . . .. . 3 . . . . .. . . 3 . . . .. . . . 3 . . .. . . . . 2 . 1. . . . 1 . 3 .. . . . . . . 3

8 Nodes:3 . . . . . . .. 3 . . . . . .. . 3 . . . . .. . . 3 . . . .. . . . 3 . . .. . . . . 3 . .. . . . 1 . 3 .. . . . . . . 3

• Epoch count: 500, none had time to converge

27

• Full convergence• 4 Nodes

• Perfect performance with 5+ hidden nodes

3 0 0 0 0 0 0 00 3 0 0 0 0 0 00 0 3 0 0 0 0 00 0 0 2 1 0 0 00 0 0 0 3 0 0 00 0 0 0 0 3 0 00 0 0 0 1 0 3 00 0 0 0 0 0 0 3


28

• Epochs to converge vs hidden nodes


4 5 6 7 8 9 10 11 12 13 14 15 16 32 64 128 256100

1000

10000

29


• Color Assignment and Original Mosaic

30

• 8 hidden neurons


31

• 16 hidden neurons


32


33


34


35

• GLCM features better than Histogram features• Both types combined work out pretty good• PWC and MLP can achieve the same quality

– Not necessarily true for less separated textures• PWC has only one modifiable parameter

– Make no difference on normalized features!• MLP has 3 parameters

– Minimal error rate is crucial– Lambda mostly affects convergence rate– Hidden layers seem to need at least class count

• Trouble converging with too few

Summary

36

• Created texture recognizer• Computed 9 features– 5 from histogram– 4 from GLCM

• Employed two different classifiers– PWC

• No parameters

– MLP• Several parameters to tweak

• UI allows to work with multiple files

Conclusion

37

Demo

38

1. Robert M Haralick, K Shanmugam, Its'hak Dinstein (1973). "Textural Features for Image Classification".

2. Emanuel Parzen (1962). On estimation of a probability density function and mode.3. Warren S. McCulloch and Walter Pitts (1943). A logical calculus of ideas immanent in

nervous activity.4. Robert Hecht-Nielsen (1989). Theory of the backpropagation neural network.5. Lee K. Jones (1990). Constructive approximations for neural networks by sigmoidal

functions.6. Keinosuke Fukunaga (1990). Introduction to Statistical Pattern Recognition.7. R. Rojas: Neural Networks, Springer-Verlag, Berlin, 19968. Claude Mbusa Takenga, Koteswara Rao Anne, K. Kyamakya, Jean Chamberlain Chedjou:

Comparison of Gradient descent method, Kalman Filtering and decoupled Kalman in training Neural Networks used for fingerprint-based positioning

9. http://www.fp.ucalgary.ca/mhallbey/the_glcm.htm10. http://www.ux.uis.no/~tranden/brodatz.html11. http://www.tek271.com/articles/neuralNet/IntoToNeuralNets.html

References

http://www.fp.ucalgary.ca/mhallbey/the_glcm.htm

http://www.ux.uis.no/~tranden/brodatz.html

http://www.tek271.com/articles/neuralNet/IntoToNeuralNets.html

Pattern Recognition & Detection: Texture Classifier ECE 4226 Presented By: Denis Petrusenko December...

Documents

Transcript of Pattern Recognition & Detection: Texture Classifier ECE 4226 Presented By: Denis Petrusenko December...