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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 VGG reading group.

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Presentation on theme: "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 VGG reading group."— Presentation transcript:

1 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 VGG reading group presentation by Minh Hoai Cumulative Attribute Space for Age and Crowd Density Estimation

2 Tasks How old are they? How many people? What is the head angle?

3 A Regression Framework Input images AAM feature Segment feature Edge feature Texture feature Feature extraction FeaturesLabel space Label (age, count) Learn the mapping Regression

4 Data distribution of FG-NET Dataset Challenge – Sparse and Unbalanced data

5 Data distribution of UCSD Dataset Challenge – Sparse and Unbalanced data

6 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

7 Cumulative Attribute Age … 20 0 … 0 the rest Cumulative attribute (dependent) Vs. 0 1 … 20th 0 … 0 Non-cumulative attribute (independent) 0 0

8 Limitation of Non-cumulative Attribute Age … 20th 0 … 0 Age 60 60th 0 … … 0 … 0 0 … st 0 1 … 0 … Age 21

9 Advantages of Cumulative Attribute Age … 20 0 … 0 the rest Age … 60 0 … … 1 … 1 attribute changes 1 1 … 21 0 … attributes change

10 Proposed Framework xixi xixi Feature vector (e.g., intensity) yiyi yiyi Label (e.g., age) … aiai aiai Cumulative attribute yiyi 1 2 The task Regressor

11 Proposed Framework xixi xixi Feature vector (e.g., intensity) yiyi yiyi Label (e.g., age) … aiai aiai Cumulative attribute yiyi 1 2 Our task Regressor How are these regressors learned? Can use any regression method: Support Vector Regression, Ridge Regression See next slide!

12 Regressor for Cumulative Attributes RegularizationRegression error # of training data Cumulative attribute Image feature vector Parameters to learn Closed-form solution:

13 Experiments

14 Baseline Methods and Name Abbreviation xixi xixi Feature vector yiyi yiyi Label Cumulative attributes Non-Cumulative attributes Support Vector Regression (SVR) SVR CA-SVR NCA-SVR … … y i … …

15 Cumulative (CA) vs. Non-cumulative (NCA) Age Estimation Mean absolute error (lower is better) Percentage of prediction within 5 years (higher is better)

16 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)

17 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

18 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?

19 OHRank - Ordinal Hyperplanes Ranker SVM score for older than k Delta 0/1 function This is 10 4 slower than closed-form solution of regression

20 Robustness Against Sparse and Unbalanced Data Age Estimation Crowd Counting (Effects of removing random/certain label groups)

21 Feature Selection by Attributes Shape plays a more important role than texture for younger ages.

22 Summary Has a simple and neat idea Exploits cumulative dependent nature of label space Addresses sparse and unbalanced data problem

23 Support Vector Regression

24 Datasets


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