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MACHINE
LEARNING I N
IMAGE
PROCESSING
PA R I N YA S A N G U A N S AT
Asst. Parinya Sanguansat, Ph.D.
Computer Engineering,
Panyapiwat Institute of Management
MACHINE LEARNING
(WITH MATLAB)
CONTENTS
• Introduction
• Feature Extraction
• Machine Learning approaches
– Image to image
– Image to non-image
• Applica...
INTRODUCTION
• Classification
• Regression
MachineData Class
Label
MachineData Data
Discrete
Continues
CLASSIFICATION VS
REGRESSION
Classification Regression
INTRODUCTION
• Supervised Learning
• Unsupervised Learning
MachineTraining
Data
learning Training
Target
learningTest Data...
SUPERVISED VS
UNSUPERVISED
Supervised Unsupervised
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 ...
VECTORIZATION
O1
O2
O3
O1 O2 O3
PROBLEMS:
• High-Dimensional feature vector
• Very large memory
• Very long processing tim...
SCALE INVARIANT FEATURE
TRANSFORM (SIFT)
• To detect and describe local features in an images, wildly used in image search...
EIGENVECTOR
https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors
𝐴𝑣 = 𝜆𝑣
BAG OF (VISUAL) WORDS
OTHER FEATURE
EXTRACTIONS
• Color
• Texture
• Shape
• Statistic
ALIGNMENT
http://www.csc.kth.se/~vahidk/face_ert.html
CLASSIFIERS
• K-NN
• Neural network
• SVM
• CNN
MACHINE
LEARNING
APPROACHES
IMAGE TO NON-IMAGE
Machine LearningImage Information
Object detection and tracking
Image recognition and classification
IMAGE TO IMAGE
Machine LearningImage Image
Image retrieval
Image enhancement
Extrapolated art (http://extrapolated-art.com...
NEURAL ARTIST STYLE
https://medium.com/@genekogan/machine-learning-for-artists-e93d20fdb097#.kf92ef5u8
http://www.kdnugget...
APPLICATIONS
FACE RECOGNITION
PreprocessingFace image Feature
Extraction
Classifier Label
PCA
Crop &
Resize
m
n
Vectorize
mn
M
M
1
1
( )( )
M
T
k k
kM 
      C
Covariance matrix
Dimension = mn x mn
( ), ...
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,...
IMAGE COVARIANCE MATRIX
• Optimization Problem: Maximize the trace of covariance matrix (Sx)
( ) { [( )( ) ]}T
xtr tr E E ...
FACE HALLUCINATION
Hallucinating
INPUT
OUTPUT
INPUT
Baseline BaselineProposed Proposed
CCTV SAMPLES
• Asian on Asian database
• European on Asian database
OBJECT DETECTION
• Viola Jones method
Positive
samples
Negative
samples
Cascade Classifier
(Adaboost)
AUGMENTED REALITY
matchFeatures
estimateGeometricTransform + imwarp
detectSURFFeatures
Create Marker
TOOLS
OPENCV
• http://opencv.org/
MATLAB
• http://www.mathworks.com/products/matlab/
ALTERNATIVE TO MATLAB
• Opensource Mostly compatible
Different Syntax
PythonBrowser-based
OCR
https://code.google.com/p/tesseract-ocr/downloads/list
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Machine learning in image processing

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

Published in: Data & Analytics
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Machine learning in image processing

  1. 1. MACHINE LEARNING I N IMAGE PROCESSING PA R I N YA S A N G U A N S AT
  2. 2. Asst. Parinya Sanguansat, Ph.D. Computer Engineering, Panyapiwat Institute of Management
  3. 3. MACHINE LEARNING (WITH MATLAB)
  4. 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. 5. INTRODUCTION • Classification • Regression MachineData Class Label MachineData Data Discrete Continues
  6. 6. CLASSIFICATION VS REGRESSION Classification Regression
  7. 7. INTRODUCTION • Supervised Learning • Unsupervised Learning MachineTraining Data learning Training Target learningTest Data classify Test Target MachineTraining Data learningTest Data cluster Data Cluster
  8. 8. SUPERVISED VS UNSUPERVISED Supervised Unsupervised
  9. 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. 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. 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. 12. EIGENVECTOR https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors 𝐴𝑣 = 𝜆𝑣
  13. 13. BAG OF (VISUAL) WORDS
  14. 14. OTHER FEATURE EXTRACTIONS • Color • Texture • Shape • Statistic
  15. 15. ALIGNMENT http://www.csc.kth.se/~vahidk/face_ert.html
  16. 16. CLASSIFIERS • K-NN • Neural network • SVM • CNN
  17. 17. MACHINE LEARNING APPROACHES
  18. 18. IMAGE TO NON-IMAGE Machine LearningImage Information Object detection and tracking Image recognition and classification
  19. 19. IMAGE TO IMAGE Machine LearningImage Image Image retrieval Image enhancement Extrapolated art (http://extrapolated-art.com/)
  20. 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. 21. APPLICATIONS
  22. 22. FACE RECOGNITION PreprocessingFace image Feature Extraction Classifier Label
  23. 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. 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. 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. 26. FACE HALLUCINATION Hallucinating INPUT OUTPUT
  27. 27. INPUT Baseline BaselineProposed Proposed
  28. 28. CCTV SAMPLES • Asian on Asian database • European on Asian database
  29. 29. OBJECT DETECTION • Viola Jones method Positive samples Negative samples Cascade Classifier (Adaboost)
  30. 30. AUGMENTED REALITY matchFeatures estimateGeometricTransform + imwarp detectSURFFeatures Create Marker
  31. 31. TOOLS
  32. 32. OPENCV • http://opencv.org/
  33. 33. MATLAB • http://www.mathworks.com/products/matlab/
  34. 34. ALTERNATIVE TO MATLAB • Opensource Mostly compatible Different Syntax PythonBrowser-based
  35. 35. OCR https://code.google.com/p/tesseract-ocr/downloads/list

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