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