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