Transcript Slides (PPT)

VGG reading group presentation by Minh Hoai
Cumulative Attribute Space for
Age and Crowd Density Estimation
Ke Chen1, Shaogang Gong1, Tao Xiang1, Chen Change Loy2
1. Queen Mary, University of London
2. The Chinese University of Hong Kong
Tasks
How old are they?
How many people?
What is the head angle?
A Regression Framework
Input images
Features
AAM feature
Feature
extraction
Segment feature
Edge feature
Texture feature
Label space
Learn the
mapping
Regression
Label (age, count)
Challenge – Sparse and Unbalanced data
Data distribution of FG-NET Dataset
Challenge – Sparse and Unbalanced data
Data distribution of UCSD Dataset
Proposed Approach
Solution:
• Attribute Learning can address data sparsity problem - Exploits the shared characteristics between classes
 Has sematic meaning
Question to address:
• How to exploit cumulative dependent nature of labels in regression?
……
……
Age 20
Age 21
……
Age 60
Cumulative Attribute
Cumulative attribute
(dependent)
0
1
…
20
1
…
Age 20
1
0
Vs.
20th
1
0
0
0
0
0
…
…
the rest
Non-cumulative attribute
(independent)
0
Limitation of Non-cumulative Attribute
0
00
1
00
0
1
0
0
1
0
0
……
0
…
0
…
…
…
0
…
Age 20
…
20th
00
0
00
21st
Age 60
21
60th
Advantages of Cumulative Attribute
1
1
1
…
…
20
1
1
0
the rest
0
…
0
1
0
1
0
0
……
0
1
…
…
Age 20
40 attributes
1 attribute
changes
change
0
21
60
60
Age 21
Proposed Framework
xi
The task
yi
Label
(e.g., age)
Feature vector
(e.g., intensity)
1
Regressor
1
1
2
…
Cumulative
attribute
yi
0
…
ai
1
0
Regressor
Proposed Framework
xi
Our task
How are these regressors learned?
yi
Label
(e.g., age)
Feature vector
(e.g., intensity)
1
Regressor
1
1
2
…
See next slide!
Cumulative
attribute
yi
0
…
ai
1
0
Regressor
Can use any regression method:
Support Vector Regression,
Ridge Regression
Regressor for Cumulative Attributes
# of training data
Regularization
Closed-form solution:
Cumulative
attribute
Image feature
vector
Regression error
Parameters
to learn
Experiments
Baseline Methods and Name Abbreviation
CA-SVR
Cumulative
attributes
1
2
1
1
…
yi
1
0
…
0
SVR
xi
yi
Support Vector Regression (SVR)
Feature vector
Label
SVR
NCA-SVR
Non-Cumulative
attributes
1
2
0
0
…
yi
1
0
…
0
Cumulative (CA) vs. Non-cumulative (NCA)
Mean absolute error
(lower is better)
Percentage of prediction within 5 years
(higher is better)
Age Estimation
Cumulative (CA) vs. Non-cumulative (NCA)
Mean absolute error
(lower is better)
Mean squared error
(lower is better)
Crowd Counting
Mean deviation error
(lower is better)
Crowd Counting Results
Based on regression
Ridge Regression without attributes
Proposed method, RR: Ridge Regression
CA-RR: our method; LSSVR: Suykens et al, IJCNN, 2001; KRR: An et al, CVPR, 2007; RFR: Liaw et al, R News, 2002;
GPR: Chan et al, CVPR, 2008; RR: Chen et al, BMVC, 2012;
Age Estimation Results
Not based on regression
What is OHRank?
Proposed method, SVR: Support Vector Regression
CA-SVR: our method; AGES: Geng et al, TPAMI, 2007; RUN: Yan et al, ICCV, 2007; Ranking: Yan et al, ICME, 2007;
RED-SVM: Chang et al, ICPR, 2010; LARR: Guo et al, TIP, 2008; MTWGP: Zhang et al, CVPR, 2010; OHRank: Chang et
al, CVPR, 2011; SVR: Guo et al, TIP, 2008;
OHRank - Ordinal Hyperplanes Ranker
Delta 0/1 function
SVM score for older than k
This is 104 slower than closed-form solution of regression
Robustness Against Sparse and Unbalanced Data
(Effects of removing random/certain label groups)
Age Estimation
Crowd Counting
Feature Selection by Attributes
Shape plays a more
important role than texture
for younger ages.
Summary
• Has a simple and neat idea
• Exploits cumulative dependent nature of label space
• Addresses sparse and unbalanced data problem
Support Vector Regression
Datasets