A Tutorial on Support Vector Machines for Pattern Recognition and Image Categorization

1. Support Vector Machines Pattern Recognition Sergios TheodoridisKonstantinos Koutroumbas Second Edition A Tutorial on Support Vector Machines for Pattern RecognitionData Mining and Knowledge Discovery, 1998C. 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 positive example negative example d+ d-

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 ChenUniversity of New Orleans James Z. WangThe 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. HL LL k, l 2x2 coefficients LH HH Image Segmentation • Daubechies-4 wavelet transform • Moments of wavelet coefficients in various frequency bands are effective for representing texture (Unser, 1995)

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 Rj • Shape properties of each region • Normalized inertia of order 1, 2, 3 (Gersho, 1979)

23. Image Representation • Shape feature of region Rj as • An image Bi • Segmentation: {Rj : j = 1, …, Ni} • Feature vectors: { xij : j = 1, …, Ni} 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) • SVMLight[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 Chapelle et al., 1999 14.8% Andrews et al., 2003 6.8%

35. Image Categorization Experiments

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

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

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