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Deformable Template as Active Basis Zhangzhang Si UCLA Department of Statistics Ying Nian Wu, Zhangzhang Si, Chuck Flemi

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of Statistics Ying Nian Wu, Zhangzhang Si, Chuck Fleming, Song-Chun Zhu ICCV07 The work presented in this 2007 talk is outdated, see http://www.stat.ucla.edu/~ywu/AB/ActiveBasisMarkII.html for the most updated results.

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Deformable Template as Active Basis Zhangzhang Si UCLA Department of Statistics Ying Nian Wu, Zhangzhang Si, Chuck Flemi

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of Statistics Ying Nian Wu, Zhangzhang Si, Chuck Fleming, Song-Chun Zhu ICCV07 The work presented in this 2007 talk is outdated, see http://www.stat.ucla.edu/~ywu/AB/ActiveBasisMarkII.html for the most updated results CIVS, Statistics Dept.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

  2. Motivation Design a deformable template to model a set of images of a certain object category. The template can be learned from example images. 2014/6/6 CIVS, Statistics Dept. UCLA 2

  3. Related work • Representation: generative and deformable models • Sparse coding [Olshausen-Field 96] • Deformable templates [Yuille-Hallinan-Cohen 89] • Active contours [Kass-Witkin-Terzopoulos 87] • Active appearance[Cootes-Edwards-Taylor 95] • 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] CIVS, Statistics Dept. UCLA

  4. Linear additive image model Image reconstruction by matching pursuit. selected from a dictionary of Gabor wavelet elements location scale orientation • Two extensions: • Encoding a single image Simultaneously encoding a set of images; • Allow each Gabor wavelet element Bi to locally perturb. CIVS, Statistics Dept. UCLA

  5. The active basis model (Gabor elements represented by bar) “Active”: Local perturbation When encoding image Im, we use the perturbed version of Bi: CIVS, Statistics Dept. UCLA

  6. An incoming car image: Deformable template using active basis A car template (Gabor elements represented by bar) 2014/6/6 CIVS, Statistics Dept. UCLA 6

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

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

  9. Pursuing the active basis MLE: (Projected on {B1,…,Bn}) (orthogonality of {B1,…,Bn}) 2014/6/6 CIVS, Statistics Dept. UCLA 9

  10. Pursuing the active basis 2014/6/6 CIVS, Statistics Dept. UCLA 10

  11. Shared pursuit algorithm 2014/6/6 CIVS, Statistics Dept. UCLA 11

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

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

  14. 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. CIVS, Statistics Dept. UCLA

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

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

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

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

  19. Main contributions 1. An active basis model as deformable template. 2. A shared pursuit algorithm for fast learning. 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. CIVS, Statistics Dept. UCLA

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