Machine learning in image processing Predictive Analytics and Data Science Conference May 27-28 Parinya sanguansat

1. MACHINE
LEARNING I N
IMAGE
PROCESSING
PA R I N YA S A N G U A N S AT

2. Asst. Parinya Sanguansat, Ph.D.
Computer Engineering,
Panyapiwat Institute of Management

3. MACHINE LEARNING
(WITH MATLAB)

4. CONTENTS
• Introduction
• Feature Extraction
• Machine Learning approaches
– Image to image
– Image to non-image
• Applications
– Face Recognition
– Face Hallucination
– Object Detection
– Augmented Reality
• Tools

5. INTRODUCTION
• Classification
• Regression
MachineData Class
Label
MachineData Data
Discrete
Continues

6. CLASSIFICATION VS
REGRESSION
Classification Regression

7. INTRODUCTION
• Supervised Learning
• Unsupervised Learning
MachineTraining
Data
learning Training
Target
learningTest Data classify Test
Target
MachineTraining
Data
learningTest Data cluster Data
Cluster

8. SUPERVISED VS
UNSUPERVISED
Supervised Unsupervised

9. FEATURE EXTRACTION
• Normal data
• Image data
A1 A2 A3 A4 A5 A6
O1 1 2 1 1 2 3
O2 1 4 2 5 3 1
O3 2 1 5 2 1 3
O4 3 2 4 5 2 4
O1
O2
O3

10. VECTORIZATION
O1
O2
O3
O1 O2 O3
PROBLEMS:
• High-Dimensional feature vector
• Very large memory
• Very long processing time
• Singularity problem
• Small Sample Size problem

11. SCALE INVARIANT FEATURE
TRANSFORM (SIFT)
• To detect and describe local features in an images, wildly used in image search,
object recognition, video tracking, gesture recognition, etc.
• Speeded Up Robust Features (SURF)

12. EIGENVECTOR
https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors
𝐴𝑣 = 𝜆𝑣

13. BAG OF (VISUAL) WORDS

14. OTHER FEATURE
EXTRACTIONS
• Color
• Texture
• Shape
• Statistic

15. ALIGNMENT
http://www.csc.kth.se/~vahidk/face_ert.html

16. CLASSIFIERS
• K-NN
• Neural network
• SVM
• CNN

17. MACHINE
LEARNING
APPROACHES

18. IMAGE TO NON-IMAGE
Machine LearningImage Information
Object detection and tracking
Image recognition and classification

19. IMAGE TO IMAGE
Machine LearningImage Image
Image retrieval
Image enhancement
Extrapolated art (http://extrapolated-art.com/)

20. NEURAL ARTIST STYLE
https://medium.com/@genekogan/machine-learning-for-artists-e93d20fdb097#.kf92ef5u8
http://www.kdnuggets.com/2015/09/deep-learning-art-style.html

21. APPLICATIONS

22. FACE RECOGNITION
PreprocessingFace image Feature
Extraction
Classifier Label

23. PCA
Crop &
Resize
m
n
Vectorize
mn
M
M
1
1
( )( )
M
T
k k
kM 
      C
Covariance matrix
Dimension = mn x mn
( ), 1,2,3, ,T
i i d y = x max( )d MPC
Scalar

24. 2DPCA
Crop &
Resize
m
n
VectorizeM
1
1
( ) ( )
M
T
k k
kM 
  G A A A A
Image covariance matrix
Dimension = n x n
, 1,2,3, ,i i i dY = Ax max( )d nPCV
Vector

25. IMAGE COVARIANCE MATRIX
• Optimization Problem: Maximize the trace of covariance matrix (Sx)
( ) { [( )( ) ]}T
xtr tr E E E  S Y Y Y Y
( ) { [( )( ) ]}
{ [( ) ( ) ]}
{ [ ( ) ( ) ]}
{ [( ) ( )] }
{ }
T
x
T T
T T
T T
T
tr tr E E E
tr E E E
tr E E E
tr E E E
tr
  
  
  
  

S Y Y Y Y
A A XX A A
X A A A A X
X A A A A X
X GX
Y = AX
( ) ( )tr XY tr YX
1
1
( ) ( )
M
T
k k
kM 
  G A A A A

26. FACE HALLUCINATION
Hallucinating
INPUT
OUTPUT

27. INPUT
Baseline BaselineProposed Proposed

28. CCTV SAMPLES
• Asian on Asian database
• European on Asian database

29. OBJECT DETECTION
• Viola Jones method
Positive
samples
Negative
samples
Cascade Classifier
(Adaboost)

30. AUGMENTED REALITY
matchFeatures
estimateGeometricTransform + imwarp
detectSURFFeatures
Create Marker

31. TOOLS

32. OPENCV
• http://opencv.org/

33. MATLAB
• http://www.mathworks.com/products/matlab/

34. ALTERNATIVE TO MATLAB
• Opensource Mostly compatible
Different Syntax
PythonBrowser-based

35. OCR
https://code.google.com/p/tesseract-ocr/downloads/list