1. CIVS, Statistics Dept. UCLA 1 2015-8-22 Deformable Template as Active Basis Zhangzhang Si UCLA Department of Statistics Ying Nian Wu, Zhangzhang Si, Chuck Fleming, Song-Chun Zhu ICCV07 The work presented in this 2007 talk is outdated, see http://www.stat.ucla.edu/~ywu/AB/ActiveBasisMarkII.html for the most updated results

2. CIVS, Statistics Dept. UCLA 2 2015-8-22 Design a deformable template to model a set of images of a certain object category. The template can be learned from example images. Motivation

3. CIVS, Statistics Dept. UCLA 3 2015-8-22 Representation: generative and deformable models 1.Sparse coding [Olshausen-Field 96] 2.Deformable templates [Yuille-Hallinan-Cohen 89] 3.Active contours [ Kass-Witkin-Terzopoulos 87] 4.Active appearance [Cootes-Edwards-Taylor 95] 5.Texton model [Zhu et.al. 02] Computation: learning and pursuit algorithm 1. Matching pursuit [Mallat and Zhang 93] 2. HMAX [Riesenhuber-Poggio 99, Mutch-Lowe 06] 3. Adaboost [Freund-Shapire 96, Viola-Jones 99] Related work

4. CIVS, Statistics Dept. UCLA 2015-8-22 selected from a dictionary of Gabor wavelet elements Linear additive image model Image reconstruction by matching pursuit. 4 Two extensions : 1.Encoding a single image Simultaneously encoding a set of images; 2.Allow each Gabor wavelet element B i to locally perturb. locationscaleorientation

5. CIVS, Statistics Dept. UCLA 5 2015-8-22 The active basis model “Active”: Local perturbation When encoding image I m, we use the perturbed version of B i : (Gabor elements represented by bar)

6. CIVS, Statistics Dept. UCLA 6 2015-8-22 Deformable template using active basis A car template An incoming car image: (Gabor elements represented by bar)

7. CIVS, Statistics Dept. UCLA 7 2015-8-22 Deformable template using active basis A car template Deformed to fit many car instances

8. CIVS, Statistics Dept. UCLA 8 2015-8-22 Learning the template: pursuing the active basis Example images B1 B3 B2 # Gabor elements selected q(I) : background distribution (all natural images) p(I) : pursued model to approximate the true distribution.

9. CIVS, Statistics Dept. UCLA 9 2015-8-22 Pursuing the active basis MLE: (Projected on {B 1,…,B n }) (orthogonality of {B 1,…,B n })

10. CIVS, Statistics Dept. UCLA 10 2015-8-22 Pursuing the active basis

11. CIVS, Statistics Dept. UCLA 11 2015-8-22 Shared pursuit algorithm

12. CIVS, Statistics Dept. UCLA 12 2015-8-22 Learning the template: pursuing the active basis Car instances A car template consisting of 60 Gabor elements

13. CIVS, Statistics Dept. UCLA 2015-8-22 37 training images, listed in the descending order of log-likelihood ratio 4.3 seconds (Core 2 Duo 2.4GHz), after convolution Experiment 1: learning an active basis model of vehicle 13 template

14. CIVS, Statistics Dept. UCLA 14 2015-8-22 Experiment 2: learning without alignment Active basis pursuit + EM Given bounding box for the first example for initialization. Iterate: - Estimate the bounding boxes using current model. - Re-learn the model from estimated bounding boxes.

15. CIVS, Statistics Dept. UCLA 15 2015-8-22 Experiment 3: learning and clustering Learning active basis EM clustering

16. CIVS, Statistics Dept. UCLA 2015-8-22 Experiment 4: car detection with active basis model map of LLR at optimal scaleMaximum LLR over scale Scan bounding box over the image at multi-resolutions Compute log-likelihood ratio by combining responses from active basis 16 LLR: log likelihood ratio

17. CIVS, Statistics Dept. UCLA 17 2015-8-22 Experiment 5: head-and-shoulder recognition Some positives Some negatives Negatives include various in-door and out door scenes, with and without human Human head and shoulders, roughly aligned Features: using the same set of Gabor filters. 43 training positives, 157 training negatives 88 testing positives, 474 testing negatives

18. CIVS, Statistics Dept. UCLA 18 2015-8-22 Experiment 5: head-and-shoulder recognition comparing with Adaboost ROC of sigmoid model is a further improvement of the result presented in the paper.

19. CIVS, Statistics Dept. UCLA 19 2015-8-22 1. An active basis model as deformable template. 2. A shared pursuit algorithm for fast learning. Main contributions http://www.stat.ucla.edu/~ywu/ActiveBasis.html Download 1) Training and testing images 2) Matlab and mex-C source codes that reproduce all the experiments in the paper and powepoint.