Presented By
Dr. Keith Haynes
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Introduction
Appearance-Based Approach
Features
Classifiers
Face Detection Walkthrough
Questions
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Computer vision is a field that includes
methods for acquiring, processing, analyzing,
and understanding images.
What does that mean?
What are some computer vision task?
Are there any faces in this image?
Class
Label
+
Test
Subject
Database of Classes
Sensing
Classification
Preprocessing
Post-Processing
Feature Extraction
Decision
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Images vary due to the relative camera-object
pose
Frontal, profile, etc.
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Components may vary in:
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Size
Shape
Color
texture
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Some objects have the ability to change
shape
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There are many possible objects
Scale
Orientation
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As the dimensions increase, the volume of
the space increases exponentially
The data points occupy a volume that is
mainly empty.
Under these conditions, tasks such as
estimating a probability distribution
function become very difficult.
In high dimensions the training sets may
not provide adequate coverage of the
space.
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Machine learning is the science of getting
computers to act without being explicitly
programmed.
Applications
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self-driving cars
speech recognition
effective web search
understanding of the human genome
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Model-Based
◦ Uses 3D models to generate images
◦ Original and rendered images compared for
classification
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Appearance-Based
◦ Learns how to classify image via training examples
Learn
Set of
Discriminatory
Features
Training Set
Perform
Feature Extraction
Feature
Representation of
Test Subject
Test Subject
Classify
Quickly & Accurately
Class
Label
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Features are learned through example
images, usually known as a training set
3D Models are not needed
Utilizes machine learning and statistical
analysis
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A feature is a calculation performed on a
portion of an image that yields a number
Features are used to represent the entity
being analyzed.
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Computes the
difference between
sums of two or
more areas
Edge detector
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262
+ -

Feature representation is determined by:
◦ the task being performed
◦ performance constraints such as accuracy and
calculation time.

Two Groups
◦ Global – feature uses the entire image
◦ Local – feature uses parts of the image



Attempts to identify the critical areas from
a set of images for class discrimination
How are critical areas identified?
Requires an exhaustive search of possible
sub-windows
Height
1
2
3
4
24
128
256
512
Width
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3
4
24
128
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512
Possible Features
1
9
36
100
90,000
68,161,536
1,082,146,816
17,247,043,584
Time
< ms
< ms
< ms
< ms
0.2 ms
0.1 sec
1.19 sec
18.48 sec
2MP image has 922,944,480,000 possible features and took 16.45 min

A single Haar feature is a weak classifier

A set of features can form a strong classifier
Features in Set Number of Sets
1
90,000
2
8,099,910,000
3
7.28976E+14
4
6.56056E+19
5
5.90424E+24
6
5.31352E+29
7
4.78185E+34
8
4.30333E+39
9
3.87266E+44
10
3.48504E+49

Exhaustive Search
◦ For 5 features 5.9x1024 unique sets

Find best features one at a time.
◦ Find the first best feature
◦ Find the feature that works best with the first
feature, and so on
◦ For 5 features 449,990 sets searched

Increase step size
Together they form a strong classifier
15
15
32
44
57
84
138
219
244
248
248
248
248
246
244
242
223
222
233
244
245
223
160
74
9
14
36
50
57
81
119
128
208
244
250
248
251
221
153
145
158
191
209
228
217
177
133
62
36
27
54
87
106
121
149
169
133
126
160
222
226
171
150
182
177
175
176
179
172
158
122
35
27
56
100
124
144
155
144
147
86
42
64
165
190
152
188
212
173
162
187
198
196
174
110
40
11
69
97
97
105
112
91
80
46
15
41
157
186
146
182
160
113
100
152
188
202
188
119
40
22
52
41
31
29
28
36
43
5
2
52
173
187
122
135
79
47
19
52
90
131
168
142
35
29
33
20
19
23
27
46
69
52
11
33
146
174
115
99
48
35
31
31
52
97
148
150
74
17
41
48
72
104
122
153
237
235
56
33
162
242
175
73
91
113
152
181
197
201
192
167
134
17
41
74
80
76
75
106
224
235
51
36
165
251
183
71
120
103
136
194
208
199
195
171
130
29
54
94
107
101
94
122
212
119
30
46
168
251
225
167
148
141
125
175
190
180
176
154
116
44
72
93
100
104
113
111
80
33
11
43
163
242
228
182
108
163
157
156
143
150
166
141
107
50
99
142
126
108
110
79
10
52
37
54
166
243
229
194
140
163
157
155
147
140
132
111
94
43
103
161
165
158
160
116
20
97
84
81
173
244
234
215
200
178
160
165
166
147
120
102
94
33
84
142
191
224
234
185
53
125
110
76
160
240
223
194
211
202
184
171
164
154
137
119
109
35
76
165
222
243
230
159
73
127
101
50
139
230
201
155
195
189
183
171
171
160
139
128
122
42
89
186
230
231
177
62
25
27
61
100
159
196
191
178
167
135
153
165
183
173
146
141
126
50
138
191
173
138
97
35
10
10
42
83
142
166
131
83
56
68
71
136
187
192
176
154
108
38
133
116
83
78
64
29
14
15
61
119
182
189
135
82
51
50
54
66
148
198
184
167
112
28
96
89
81
93
81
37
22
69
109
157
190
203
196
171
148
74
67
49
107
167
179
167
93
26
77
127
157
160
114
34
13
77
150
200
209
215
229
224
197
52
40
68
94
129
165
151
70
22
60
159
210
191
126
44
19
40
101
145
152
161
173
164
151
76
94
145
156
155
158
122
41
14
33
134
187
170
122
71
47
33
53
91
106
125
144
131
140
171
207
227
232
207
154
86
12
6
18
74
122
143
128
85
71
77
113
164
185
204
226
225
227
235
234
239
235
196
125
49
1
0
12
39
67
111
131
95
95
96
121
127
168
212
224
225
232
245
241
245
243
175
72
19
0
Original Image
Feature
Set
47
-229
-498
179
106
-157
-346
11
24
-99
-257
423





Feature selection is important, is application
dependent
Statistical methods very useful with high
dimensionality
Local identify discriminating areas or features
images
No universal solution
Features can be combined





Linear Discriminant Analysis
Fisher Discriminant Analysis
Bayesian Classifier
Neural Networks
K-Nearest Neighbor Classifier


Features can used to form a coordinate space
called the feature space.
Euclidean distance is used as the metric
X  ( x11  x12 )  ...  ( xd1  xd 2 )
2
2




The distance is not
used directly for
feature selection
The higher the ratio,
the better the filter
In order to prevent one
class from dominating,
an exponential
function was used
The sum of function
for all test images was
used for selection
[Liu, Srivastava, Gallivan]
Separation and grouping
Better Classification
Low Classification Rates




“Divide and Conquer”
Instead of trying to solve a difficult problem
all at once, divide it into several parts
Each of the resulting parts should be easier to
solve than the original problem
Perform classifications fast
1,..,20
1,13,15,18
1,15
3,4,8,20
5,6,9,10
2,7,11,12,
14,16,17,19
13,18
7,11,
16,17
2,12
- Indicates that all
children are leaf nodes
7,16
14,19
11,17




Classical technique that is widely used for
image compression and recognition
Produces features with a dimensionality
significantly less than that of the original
images
Reduction is performed without a
substantial loss of the data contained in the
image
Analysis is based on the variance of dataset
◦ Variance implies a distinction in class
Feature
Set
47
-229
-498
179
106
-157
-346
11
24
-99
-257
423
Feature
Set
×
PCA
Matrix
478
-367
206
-358
386
Lower
Dimensional
Space



In many cases, the PCA reduction was not
sufficient
Improving the performance of the reduction
matrix is necessary
Four methods were implemented
◦ Gradient Search
◦ Random or Vibration Search
 Variation of the Metropolis Algorithm
◦ Neighborhood Component Analysis
◦ Stochastic Gradient Search

Data reduction occurs via a matrix
multiplication
◦ x′ = xA

Optimization is achieved by
◦ defining F as a function A, F(A)
◦ Changing A


Can be computationally expensive
Does not provide a means to escape a local
maximum
local
maximum
global
maximum
f(x)
x



Makes a guess
Guesses are fast
There is a possibility of escaping a local
maximum
local
maximum
global
maximum
f(x)
x

Restricting the search area increases the
probability of finding an increasing path

If all data at the node can be classified accurately
◦ The classification decision is stored as leaf nodes
◦ No further processing occurs down this branch of the tree.

If data cannot be classified accurately
◦ The problem then becomes a clustering one.
◦ Accuracy is now defined in terms of clusters, not classes.

The accuracy achieved through class level
clustering will always be no worse than that of
individual classes and in most cases, will be higher.
Feature Space
Decision Boundary
Prior to Clustering
Decision Boundary
After Clustering
R
A
3
B
D
6
2
E
1
4
8
C
5
9
10
7
R
A
3
B
D
2
E
Feature Space
Root
A
B
Decision Boundary
After Clustering
1
4
8
C
6
5
9
10
7
R
A
3
B
D
2
E
Feature Space
C
D
E
Decision Boundary
After Clustering
1
4
8
C
6
5
9
10
7
Classifier
KNN
Neural Network
SVM
RCT
KNN
Neural Network
SVM
RCT
SVM
RCT
Dataset
Accuracy
Throughput
ORL
100%
92.50%
86.25%
97%
16,735
53,522
7,251
982,110
COIL
100%
89%
97.75%
100%
19,204
53,709
3,790
3,781,933
Breast Cancer
96.96%
97.35%
141,240
8,949,638
X. Liu, A. Srivastava, K. Gallivan, Optimal linear representations
of images for object recognition, IEEE Transactions on Pattern
Analysis and Machine Intelligence, May 2004, pp. 662-666.
Haynes, K., Liu, X., Mio, W., (2006). Object Recognition Using
Rapid Classification Trees. 2006 International Conference on
Image Processing
Haynes, K. (2011). Using Image Steganography to Establish
Covert Communication Channels. International Journal of
Computer Science and Information Security, Vol. 9, No. 9
Duda, R., Hart, P., Stork, D. (2001). Pattern Classification. WileyInterscience Publications, NY