ke chen 1 , shaogang gong 1 , tao xiang 1 , chen change loy 2
DESCRIPTION
VGG reading group presentation by Minh Hoai. Ke Chen 1 , Shaogang Gong 1 , Tao Xiang 1 , Chen Change Loy 2 1. Queen Mary, University of London 2. The Chinese University of Hong Kong. Cumulative Attribute Space for Age and Crowd Density Estimation. Tasks. How old are they?. - PowerPoint PPT PresentationTRANSCRIPT
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Ke Chen1, Shaogang Gong1, Tao Xiang1, Chen Change Loy2
1. Queen Mary, University of London2. The Chinese University of Hong Kong
VGG reading group presentation by Minh Hoai
Cumulative Attribute Space for Age and Crowd Density Estimation
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Tasks
How old are they?
How many people?
What is the head angle?
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A Regression FrameworkInput images
AAM feature
Segment feature
Edge feature
Texture feature
Feature extraction
Features Label space
Label (age, count)Learn the mapping
Regression
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Data distribution of FG-NET Dataset
Challenge – Sparse and Unbalanced data
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Data distribution of UCSD Dataset
Challenge – Sparse and Unbalanced data
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Proposed Approach
Solution:• Attribute Learning can address data sparsity problem --
Exploits the shared characteristics between classesHas sematic meaning
Question to address:• How to exploit cumulative dependent nature of labels in
regression?
…… …… ……
Age 20 Age 21 Age 60
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Cumulative Attribute
Age 20
1
1
0
1
…20
0…0
the rest
Cumulative attribute (dependent)
Vs.
0
1
…
20th
0…
0
Non-cumulative attribute (independent)
0
0
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Limitation of Non-cumulative Attribute
Age 200
1
…
20th
0…0
Age 60
60th0
…
0
0
0
1
…
0…
0
0…0
0
21st
0
1
…
0
…
0
0
0
0
Age 21
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Age 21
Advantages of Cumulative Attribute
Age 20
1
1
0
1
…20
0…0
the rest
Age 60
1
1
1
…60
0…
0
1
0
… 1…1 attribute changes
1
1…21
0
…
0
1
1
0
40 attributes change
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Proposed Framework
xiFeature vector (e.g., intensity)
yiLabel
(e.g., age)
1
1
1
… …0
0
aiCumulative
attribute
yi
12
The task
Regressor Regressor
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Proposed Framework
xiFeature vector (e.g., intensity)
yiLabel
(e.g., age)
1
1
1
… …0
0
aiCumulative
attribute
yi
12
Our task
Regressor Regressor
How are these regressors learned?
Can use any regression method: Support Vector Regression, Ridge Regression
See next slide!
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Regressor for Cumulative Attributes
Regularization Regression error
# of training dataCumulative
attribute Image feature
vector Parameters to learn
Closed-form solution:
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Experiments
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Baseline Methods and Name Abbreviation
xiFeature vector
yiLabel
Cumulative attributes
Non-Cumulative attributes
Support Vector Regression (SVR)
SVR
SVR
CA-SVR
NCA-SVR
…1 1 0 0…1
1 2 yi
…1 0 0…00
1 2 yi
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Cumulative (CA) vs. Non-cumulative (NCA)
Age Estimation
Mean absolute error(lower is better)
Percentage of prediction within 5 years(higher is better)
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Cumulative (CA) vs. Non-cumulative (NCA)
Crowd Counting
Mean absolute error(lower is better)
Mean squared error(lower is better)
Mean deviation error(lower is better)
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Crowd Counting Results
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;
Based on regression
Proposed method, RR: Ridge Regression
Ridge Regression without attributes
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Age Estimation Results
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;
Proposed method, SVR: Support Vector Regression
Not based on regression
What is OHRank?
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OHRank - Ordinal Hyperplanes Ranker
SVM score for older than k
Delta 0/1 function
This is 104 slower than closed-form solution of regression
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Robustness Against Sparse and Unbalanced Data
Age Estimation
Crowd Counting
(Effects of removing random/certain label groups)
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Feature Selection by Attributes
Shape plays a more important role than texture for younger ages.
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Summary
• Has a simple and neat idea • Exploits cumulative dependent nature of label space• Addresses sparse and unbalanced data problem
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Support Vector Regression
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Datasets