1. Two Feature Extraction Methods
Lian, Xiaochen
skylian1985@163.com
Department of Computer Science
Shanghai Jiao Tong University
July 13, 2007
Lian, Xiaochen Two Feature Extraction Methods

2. Attention Based Method
Statistics Based Method
Outline
1 Attention Based Method
Why Attention?
Model of Attention
Application in Face Recognition
2 Statistics Based Method
Basic Idea
Feature Selection Process
Lian, Xiaochen Two Feature Extraction Methods

3. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Outline
1 Attention Based Method
Why Attention?
Model of Attention
Application in Face Recognition
2 Statistics Based Method
Basic Idea
Feature Selection Process
Lian, Xiaochen Two Feature Extraction Methods

4. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Why Attention?
When recognizing a person, we compare the face with those
stored in memory. We always can not remember all the details of a
face. It is the conspicuous parts that impress themselves on us.
Lian, Xiaochen Two Feature Extraction Methods

5. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Model of Attention
How do human vision system find salient regions in a scene? Koch
and Ullman[?] proposed a biologically plausible architecture.
Figure: General architecture ofExtraction Methods
Lian, Xiaochen Two Feature
the model

6. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Channels
The original image is decomposed into three channels.
Intensity I : Consider the brightness of a pixel, which is
obtained as I = (r + g + b)/3.
Color: Red-Green color and Blue-Yellow opponencies.
r−g
RG =
max(r, g, b)
b − min(r, g)
BY =
max(r, g, b)
Orientation: Four orientation channels correspond to gabor
filters oriented at 0, 45, 90, and 135 degrees. This
representation is able to capture the critical distinctions in
orientation.
Lian, Xiaochen Two Feature Extraction Methods

7. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Channels
Figure: Channels
Lian, Xiaochen Two Feature Extraction Methods

8. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Image Pyramid
The Gaussian pyramid is created to a depth of nine levels, with
level 0 having a scale of 1 : 1 (the original input image) and level 8
being 1 : 256. This is done by filtering the images with gaussian
filter and then resize it. We use gaussian filter to eliminate noise,
and the resizing is for biological purpose.
There are seven pyramids, one for intensity MI , two for color MRG
and MBY , and four for orientation Mθ (θ ∈ {0, 45, 90, 135}).
Lian, Xiaochen Two Feature Extraction Methods

9. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Image Pyramid
Figure: Image pyramid
Lian, Xiaochen Two Feature Extraction Methods

10. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Center Surround Difference
It is a cross-scale difference between two images, denoted by “ ”:
expanding the smaller image into the larger one by interpolation,
then followed by pixel-pixel substraction.
Figure: Center Surround Difference
Lian, Xiaochen Two Feature Extraction Methods

11. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Normalization
Figure: Normalization effect
Lian, Xiaochen Two Feature Extraction Methods

12. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Normalization
Difference-of-Gaussians filter is usually used to detect blob.
c2 −(x2 +y2 )/(2σ2 ) c2
e−(x +y )/(2σinh )
2 2 2
DoG(x, y) = ex
2
e ex − inh
2
2πσex 2πσinh
Lian, Xiaochen Two Feature Extraction Methods

13. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Saliency Map
Combine the images from all the channels linearly.
Figure: Saliency Map
Lian, Xiaochen Two Feature Extraction Methods

14. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Figure: Faces and the corresponding saliency Map(from ORL face
database)
Lian, Xiaochen Two Feature Extraction Methods

15. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Experiment Result
Figure: Error rate
Figure: Rank error rate
Lian, Xiaochen Two Feature Extraction Methods

16. Why Attention?
Attention Based Method
Model of Attention
Statistics Based Method
Application in Face Recognition
Lots of Problems!
How to do recognition? Different people have different sets of
features. Simply applying Euclid Distance yields bad
performance: the error rate is high for a 40-person database.
The performance suffers pose and expression severely.
Lian, Xiaochen Two Feature Extraction Methods

17. Attention Based Method Basic Idea
Statistics Based Method Feature Selection Process
Outline
1 Attention Based Method
Why Attention?
Model of Attention
Application in Face Recognition
2 Statistics Based Method
Basic Idea
Feature Selection Process
Lian, Xiaochen Two Feature Extraction Methods

18. Attention Based Method Basic Idea
Statistics Based Method Feature Selection Process
Basic Idea
Suppose S = {x1 , x2 , · · · , xn } be n features for the collected data.
The objective of feature selection is to find a subset
Sd = {xi1 , xi2 , · · · , xid }, which suffice to represent the original data.
The performance of Sd can be evaluated by the percentage of the
variation in xi that can be accounted for by the elements by Sd . If
that percentage is large enough, Sd can then be the final choice;
otherwise, new significant variables need to be added into Sd .
Lian, Xiaochen Two Feature Extraction Methods

19. Attention Based Method Basic Idea
Statistics Based Method Feature Selection Process
Feature Similarity Measure
The squared-correlation coefficient between two random vectors x
and y is
(xt y)2
sc(x, y) = .
(xt x)(yt y)
This measure has the following properties:
0 ≤ |sc(x, y)| ≤ 1.
|sc(x, y)| if and only if x and y are linearly related.
The measure is invariant to scaling and translation.
The measure is sensitive to rotation.
Lian, Xiaochen Two Feature Extraction Methods

20. Attention Based Method Basic Idea
Statistics Based Method Feature Selection Process
Step-By-Step Selection
At the first step, let
i=1 sc(xi , xj )
n
Cj,1 = ,
n
i1 = arg max {Cj,1 }.
1≤j≤n
Select xi1 as the first significant variable.
Lian, Xiaochen Two Feature Extraction Methods

21. Attention Based Method Basic Idea
Statistics Based Method Feature Selection Process
Step-By-Step Selection
Assume the first m − 1 most significant variables, z1 , · · · , zm−1 , has
been chosen. The m-th significant feature zm will be chosen in such
a manner: The subset Sm−1 + {zm } should be the most
representative subset compared with any other subsets formed by
adding a candidate feature to Sm−1 .
Let αj ∈ S − Sm−1 and
i=1 sc(xi , αj )
n
Cj,m = ,
n
im = arg max {Cj,m }.
1≤j≤n
The m-th significant feature can then be xim .
Lian, Xiaochen Two Feature Extraction Methods

22. Attention Based Method Basic Idea
Statistics Based Method Feature Selection Process
Lian, Xiaochen Two Feature Extraction Methods

23. Attention Based Method Basic Idea
Statistics Based Method Feature Selection Process
Some Discussion
The squared-correlation coefficient is used to measure the
linear correlation between variables. Need new method for
nonlinear relationships.
The greedy search process do not assure the optimal
selection.
The complexity is O(n2 N), where n is the number of features,
and N is the number of samples. When n become large, the
algorithm will be inefficient.
Lian, Xiaochen Two Feature Extraction Methods

24. Attention Based Method Basic Idea
Statistics Based Method Feature Selection Process
C. Koch, and S. Ullman, “Shifts in Selective Visual Attention:
Towards the Underlying Neural Circuitry,” Human Neurobiology, vol.
4, pp. 219-227, 1985. pp. 89-102, 1977.
Laurent Itti, and Christof Koch, “A saliency-based search mechanism
for overt and covert shitfs of visual attention,” Vision Research,
40(2000).
Dirk Walther, and Christof Koch, “Modeling attention to salient
proto-objects,” Neural Networks, 19(2006).
Laurent Itti, Christof Koch, and Ernst Niebur, “A Model of
Saliency-Based Visual Attention for Rapid Scene Analysis,” IEEE
Trans. Pattern Analysis and Machine Intelligence, vol. 20, No. 11,
Nov. 1998.
Lian, Xiaochen Two Feature Extraction Methods

25. Attention Based Method Basic Idea
Statistics Based Method Feature Selection Process
Hua-Liang, and Stephen A. Billings, “Feature Subset Selection
and Ranking for Data Dimensionality Reduction,” IEEE Trans.
Pattern Analysis and Machine Intelligence, vol. 29, no.1, Jan.
2007.
Pabitra Mitra, C.A. Mrthy, and Sankar K. Pal “Unsupervised
Feature Selection Using Feature Similarity,” IEEE Trans.
Pattern Analysis and Machine Intelligence, vol. 24, No. 3, Mar.
2002.
Lian, Xiaochen Two Feature Extraction Methods