讲解人 : 崔 振 2010.9.17

1. 讲解人: 崔 振 2010.9.17 Supervised Translation-Invariant Sparse Coding [Jianchao Yang, Kai Yu, Thomas Huang]

2. 提纲 • 作者信息 • 文章信息 • 拟解决的问题 • 本文的方法 • 实验 • 结论

3. 提纲 • 作者信息 • 文章信息 • 拟解决的问题 • 本文的方法 • 实验 • 结论

4. Jianchao Yang • Image Formation & Processsing Group (IFP), University of Illinois at Urbana-Champaign (UIUC) • Ph.D. Candidate (06-Present, ECE, UIUC) ; Ph.D. Adviser: Prof. Thomas S. Huang • B.Eng (02-06, EEIS, USTC) • Publication（第一作者） • CVPR：4篇，2篇oral • TIP：2篇 • ECCV10，1篇 • ICIP,1篇 • Homepage: http://www.ifp.illinois.edu/~jyang29/ jyang29 @ifp.uiuc.edu

5. Kai Yu • Machine Learning researcher and the Head of Media Analytics Department at NEC Laboratories America. Inc.. • Ph.D. Computer Science, University of Munich,Germany, January 2001 – July 2004. • B.Sc and M.Sc, Nanjing University. • Research Interests • Areas: machine learning, data mining, information retrieval, computer vision • CVPR(4),ECCV(4+),ICML(8+),NIPS(10+),… • http://www.dbs.informatik.uni-muenchen.de/~yu_k/

6. Thomas Huang • Beckman Institute Image Formation and Processing and Artificial Intelligence groups. • William L. Everitt Distinguished Professor in the U of I Department of Electrical and Computer Engineering and the Coordinated Science Lab (CSL); • Sc.D. from MIT in 1963 • computer vision, image compression and enhancement, pattern recognition, and multimodal signal processing. • http://www.beckman.illinois.edu/directory/t-huang1

7. 提纲 • 作者信息 • 文章信息 • 拟解决的问题 • 本文的方法 • 实验 • 结论

8. 文章信息 • 文章出处 • CVPR10（oral） • 相关文章 • Yang et al. Linear spatial pyramid matching using sparse coding for image classification. CVPR’09.

9. Abstract • In this paper, we propose a novel supervised hierarchical sparse coding model based on local image descriptors for classification tasks. The supervised dictionary training is performed via back-projection, by minimizing the training error of classifying the image level features, which are extracted by max pooling over the sparse codes within a spatial pyramid. Such a max pooling procedure across multiple spatial scales offer the model translation invariant properties, similar to the Convolutional Neural Network (CNN). Experiments show that our supervised dictionary improves the performance of the proposed model significantly over the unsupervised dictionary, leading to state-of-the-art performance on diverse image databases. Further more, our supervised model targets learning linear features, implying its great potential in handling large scale datasets in real applications.

10. 摘要 • 针对分类任务，提出了一种新颖的基于局部图像描述子的监督分级稀疏编码模型。 • 通过back-projection方法，以最小化在图像层级特征(image level features)的分类误差训练监督词典。其中图像层级特征是以空间金字塔为结构max pooling稀疏编码。在多种空间尺度下max pooling方法具有平移不变的特性，如同CNN(Convolutional Neural Network)一样。 • 实验证明，与无监督词典相比，监督词典明显地改善了模型的性能，并且在多个图像数据库拥有最好的表现。 • 另外，监督模型目标是学习线性特征，它蕴含了一个巨大潜能-实时地处理大规模数据库。

11. 提纲 • 作者信息 • 文章信息 • 拟解决的问题 • 本文的方法 • 实验 • 结论

12. 拟解决的问题 • Image classification • To find a generic feature representation • Interested in linear prediction model

13. Sparse Coding for Image Classification

14. 提纲 • 作者信息 • 文章信息 • 拟解决的问题 • 本文的方法 • 实验 • 结论

15. 本文的方法 • 框架 • 相关知识 • 本文模型 • 求解方法

16. Descriptor extraction Bag of coordinated Local descriptors nonlinear coding Yang. CVPR09 High-dimensional sparse codes feature pooling Image representation classification It must be a cool Cat! 框架 J. Yang et al. Linear spatial pyramid matching using sparse coding for image classification. CVPR’09.

17. 已有方法 • Histogram-based SPM feature • Step 1: local descriptor extraction • Step 2: vector quantization (e.g.k-means) • Step 3: hierarchical average pooling • Step 4: nonlinear SVM • The framework of ScSPM （CVPR09） • Step 1: local descriptor extraction • Step 2: sparse coding (无监督词典) • Step 3: hierarchical max pooling • Step 4: linear SVM

18. Xnxm=(X1,X2,…,Xm) Bnxk：词典 Zkxm：稀疏系数 相关知识(1) • Sparse coding • Max pooling

19. 相关知识(2) S: 尺度（层次） U: 串接 分级融合

20. 目标函数 Model（1） Xk：表示第k个图像 + SVM 多层max pooling

21. 监督 Model（2）-目标函数 Optimization over B: back propagation!

22. No analytical link Squared hinge loss function Linear prediction model Only cares about the pooled maximum values 求解方法（1）

23. Setting the gradients at zero coefficients to be zero, a lot of computations can be saved! 求解方法（2） • Solution: use implicit differentiation D. M. Bradley et al. Differentiable sparse coding. NIPS 2008.

24. Training convergence • Initialization is important: B is trained in unsupervised manner. • Convergence

25. Example dictionary • Example dictionary: CMU PIE Supervised Unsupervised

26. 提纲 • 作者信息 • 文章信息 • 拟解决的问题 • 本文的方法 • 实验 • 结论

27. Experiment • Classification tasks • Face recognition: CMU PIE, and CMU Multi-PIE • Handwritten digit recognition: MNIST • Gender Recognition: FRGC 2.0 • Image local descriptors: raw image patches • Prediction model: one-vs-all linear SVM with squared hinge loss function. • Stochastic optimization: typically converges in 10 iterations, gradient descent.

28. Experiment • Parameter settings 学习率：

29. Experiment –Face Recognition (1) • CMU PIE: • 41368 images of 68 people, each under 13 poses, 43 different illumination conditions with 4 different expressions. • A subset of five near frontal views are used including all expressions and illuminations.

30. Experiment –Face Recognition (1) • USC: unsupervised sparse coding model. • SSC: supervised sparse coding model. • Improvements: shows the improvements of SSC over USC. Classification error(%) on CMU PIE

31. Experiment –Face Recognition (2) • CMU Multi-PIE: • contains 337 subjects across simultaneous variations in pose, expression and illumination. • A subset containing near frontal view face images are used as training and testing.

32. Experiment –Face Recognition (2) Face recognition error(%) on Multi-PIE [SR] A. Wagner et al. Towards a practical face recognition system: robust registration and illumination by sparse representation. CVPR’09.

33. Experiment – Handwritten Digit Recognition • MNIST: consists of 70,000 handwritten digits, aligned to the center. 60,000 of them are modeled as training, and the rest 10,000 as testing.

34. Experiment – Gender Recognition • FRGC 2.0 • contains 568 individuals, totally 14714 face images under various lighting conditions and backgrounds. • 11700 face images of 451 individuals are used as training, and the remaining 3014 images of 114 persons are used as testing.

35. Experiment – Gender Recognition

36. 提纲 • 作者信息 • 文章信息 • 拟解决的问题 • 本文的方法 • 实验 • 结论

37. Conclusion • A supervised translation-invariant sparse coding model for image classification • A generic image representation. • The max pooling feature is translation-invariant. • Sparse coding on local descriptors is promising compared to sparse coding on holistic image. • Supervised sparse coding improves the performance significantly. • Next steps: • Connections with hierarchical models in deep belief networks should be investigated. • More theoretical analysis for pooling functions are needed. • Deep hierarchical models based on sparse coding should be studied.

38. Thank you! Q&A

39. 参考文献 • Jianchao Yang, Kai Yu, Thomas Huang,Supervised Translation-Invariant Sparse Coding. CVPR10. • J. Yang et al. Translation-Invariant Sparse Coding. CVPR10(talk). • J. Yang et al. Linear spatial pyramid matching using sparse coding for image classification. CVPR’09.