1. Support Vector Machines Pattern Recognition Sergios Theodoridis Konstantinos Koutroumbas Second Edition A Tutorial on Support Vector Machines for Pattern Recognition Data Mining and Knowledge Discovery, 1998 C. J. C. Burges

2. Separable Case

3. Maximum Margin Formulation

4. Separable Case Label the training data Hyperplane satisfy w ： normal to the hyperplane |b|/||w|| ： perpendicular distance from the hyperplane to the origin d + (d - ) ： margin

5. Separable Case d+d+ d-d- positive example negative example

6. Separable Case Suppose that all the training data satisfy the following constraints These can be combines into one set of inequalities Distance of a point from a hyperplane class 1 class 2

7. Separable Case Having a margin of Task compute the parameter w, b of the hyperplane maximize

8. Separable Case Karush-Kuhn-Tucker (KKT) conditions : vector of the Langrange multiplier : Langrangian function

9. Separable Case Wolfe dual representation form

10. Image Categorization by Learning and Reasoning with Regions Yixin Chen University of New Orleans James Z. Wang The Pennsylvania State University Journal of Machine Learning Research 5 (2004) (Submitted 7/03; Revised 11/03; Published 8/04)

11. Introduction Automatic image categorization Difficulties Variable & uncontrolled image conditions Complex and hard-to-describe objects in image Objects occluding other objects Applications Digital libraries, Space science, Web searching, Geographic information systems, Biomedicine, Surveillance and sensor system, Commerce, Education

12. Overview Give a set of labeled images, can a computer program learn such knowledge or semantic concepts form implicit information of objects contained in image?

13. Related Work Multiple-Instance Learning Diverse Density Function (1998) MI-SVM (2003) Image Categorization Color Histograms (1998-2001) Subimage-based Methods (1994-2004)

14. Motivation Correct categorization of an image depends on identifying multiple aspects of the image Extension of MIL→A bag must contain a number of instances satisfying various properties

15. A New Formulation of Multiple-Instance Learning Maximum margin problem in a new feature space defined by the DD function DD-SVM In the instance feature space, a collection of feature vectors, each of which is called an instance prototype, is determined according to DD

16. A New Formulation of Multiple-Instance Learning Instance prototype: A class of instances (or regions) that is more likely to appear in bags (or images) with the specific label than in the other bags Maps every bag to a point in bag feature space Standard SVMs are the trained in the bag feature space

17. Outline Image segmentation & feature representation DD-SVM, and extension of MIL Experiments & result Conclusions & future work

18. Image Segmentation Partitions the image into non- overlapping blocks of size 4x4 pixels Each feature vector consists of six features Average color components in a block LUV color space Square root of the second order moment of wavelet coefficients in high-frequency bands

19. Image Segmentation Daubechies-4 wavelet transform Moments of wavelet coefficients in various frequency bands are effective for representing texture (Unser, 1995) LL HL HHLH k, l 2x2 coefficients

20. Image Segmentation k-means algorithm: cluster the feature vectors into several classes with every class corresponding to one “region” Adaptively select N by gradually increasing N until a stopping criterion is met (Wang et al. 2001)

21. Segmentation Results

22. Image Representation :the mean of the set of feature vectors corresponding to each region R j Shape properties of each region Normalized inertia of order 1, 2, 3 (Gersho, 1979)

23. Image Representation Shape feature of region R j as An image B i Segmentation: {R j : j = 1, …, N i } Feature vectors: { x ij : j = 1, …, N i } 9-dimensional feature vector

24. An extension of Multiple-Instance Learning Maximum margin formulation of MIL in a bag feature space Constructing a bag feature space Diverse density Learning instance prototypes Computing bag features

25. Maximum Margin Formulation of MIL in a Bag Feature Space Basic idea of new MIL framework: Map every bag to a point in a new feature space, named the bag feature space To train SVMs in the bag feature space subject to

26. Constructing a Bag Feature Space Clues for classifier design: What is common in positive bags and does not appear in the negative bags Instance prototypes computed from the DD function A bag feature space is then constructed using the instance prototypes

27. Diverse Density (Maron and Lozano-Perez, 1998) A function defined over the instance space DD value at a point in the feature space The probability that the point agrees with the underlying distribution of positive and negative bags

28. Diverse Density It measures a co-occurrence of instances from different (diverse) positive bags

29. Learning Instance Prototype An instance prototype represents a class of instances that is more likely to appear in positive bags than in negative bags Learning instance prototypes then becomes an optimization problem Finding local maximizers of the DD function in a high-dimensional

30. Learning Instance Prototype How do we find the local maximizers? Start an optimization at every instance in every positive bag Constraints: Need to be distinct from each other Have large DD values

31. Computing Bag Features Let be the collection of instance prototypes Bag features,

32. Experimental Setup for Image Categorization COREL Corp: 2,000 images 20 image categories JPEG format, size 384*256 (256*384) Each category are randomly divided into a training set and a test set (50/50) SVM Light [Joachims, 1999] software is used to train the SVMs

33. Sample Images (COREL)

34. Image Categorization Performance 5 random test sets, 95% confidence intervals The images belong to Cat.0 ~ Cat.9 14.8% 6.8% Chapelle et al., 1999 Andrews et al., 2003

35. Image Categorization Experiments

36. Sensitivity to Image Segmentation k-means clustering algorithm with 5 different stopping criteria 1,000 images for Cat.0 ~ Cat.9

37. Robustness to Image Segmentation 6.8% 9.5%11.7% 13.8% 27.4%

38. Robustness to the Number of Categories in a Data Set 81.5% 67.5% 6.8% 12.9

39. Difference in Average Classification accuracies

40. Sensitivity to the Size of Training Images

41. Sensitivity to the Diversity of Training Images Varies

42. MUSK Data Sets

43. Speed 40 minutes Training set of 500 images (4.31 regions per image) Pentium III 700MHz PC running the Linux operating system Algorithm is implemented in Matlab, C programming language The majority is spent on learning instance prototypes

44. Conclusions A region-based image categorization method using an extension of MIL → DD-SVM Image → collection of regions → k-means alg. Image → a point in a bag feature space (defined by a set of instance prototypes learned with the DD func.) SVM-based image classifiers are trained in the bag feature space DD-SVM outperforms two other methods DD-SVM generates highly competitive results on MUSK data set

45. Future Work Limitations Region naming (Barnard et al., 2003) Texture dependence Improvement Image segmentation algorithm DD function Scene category can be a vector Semantically-adaptive searching Art & biomedical images