Topic III: Bag of Words, Dirichlet Processes, and Chinese Restaurant Processes

1. Topic III: Bag of Words, Dirichlet Processes, and Chinese Restaurant Processes Notes are due to Fei-fei Li and Michael I. Jordan

2. Computer receives telephone call Measures Pitch of voice Decides gender of caller Toy example Male Human Voice Female

3. Generative modeling mean1 mean2 var1 var2 Probability Voice Pitch

4. Discriminative approach No. of mistakes Voice Pitch

5. Suggested approach Definitely female Definitely male No. of mistakes Unsure Voice Pitch

6. Early “bag of words” models: mostly texture recognition Cula et al. 2001; Leung et al. 2001; Schmid 2001; Varma et al. 2002, 2003; Lazebnik et al. 2003 Hierarchical Bayesian models for documents (pLSA, LDA, etc.) Hoffman 1999; Blei et al, 2004; Teh et al. 2004 Object categorization Dorko et al. 2004; Csurka et al. 2003; Sivic et al. 2005; Sudderth et al. 2005; Natural scene categorization Fei-Fei et al. 2005 Related works

7. Object Bag of ‘words’

8. Analogy to documents China is forecasting a trade surplus of $90bn (£51bn) to $100bn this year, a threefold increase on 2004's $32bn. The Commerce Ministry said the surplus would be created by a predicted 30% jump in exports to $750bn, compared with a 18% rise in imports to $660bn. The figures are likely to further annoy the US, which has long argued that China's exports are unfairly helped by a deliberately undervalued yuan. Beijing agrees the surplus is too high, but says the yuan is only one factor. Bank of China governor Zhou Xiaochuan said the country also needed to do more to boost domestic demand so more goods stayed within the country. China increased the value of the yuan against the dollar by 2.1% in July and permitted it to trade within a narrow band, but the US wants the yuan to be allowed to trade freely. However, Beijing has made it clear that it will take its time and tread carefully before allowing the yuan to rise further in value. sensory, brain, visual, perception, retinal, cerebral cortex, eye, cell, optical nerve, image Hubel, Wiesel China, trade, surplus, commerce, exports, imports, US, yuan, bank, domestic, foreign, increase, trade, value Of all the sensory impressions proceeding to the brain, the visual experiences are the dominant ones. Our perception of the world around us is based essentially on the messages that reach the brain from our eyes. For a long time it was thought that the retinal image was transmitted point by point to visual centers in the brain; the cerebral cortex was a movie screen, so to speak, upon which the image in the eye was projected. Through the discoveries of Hubel and Wiesel we now know that behind the origin of the visual perception in the brain there is a considerably more complicated course of events. By following the visual impulses along their path to the various cell layers of the optical cortex, Hubel and Wiesel have been able to demonstrate that the message about the image falling on the retina undergoes a step-wise analysis in a system of nerve cells stored in columns. In this system each cell has its specific function and is responsible for a specific detail in the pattern of the retinal image.

10. learning recognition codewords dictionary feature detection & representation image representation category decision category models (and/or) classifiers

11. Representation codewords dictionary feature detection & representation image representation 2. 1. 3.

12. 1.Feature detection and representation

13. Regular grid Vogel et al. 2003 Fei-Fei et al. 2005 1.Feature detection and representation

14. Regular grid Vogel et al. 2003 Fei-Fei et al. 2005 Interest point detector Csurka et al. 2004 Fei-Fei et al. 2005 Sivic et al. 2005 1.Feature detection and representation

15. Regular grid Vogel et al. 2003 Fei-Fei et al. 2005 Interest point detector Csurka et al. 2004 Fei-Fei et al. 2005 Sivic et al. 2005 Other methods Random sampling (Ullman et al. 2002) Segmentation based patches (Barnard et al. 2003) 1.Feature detection and representation

16. 1.Feature detectionand representation Compute SIFT descriptor [Lowe’99] Normalize patch Detect patches [Mikojaczyk and Schmid ’02] [Matas et al. ’02] [Sivic et al. ’03] Slide credit: Josef Sivic

17. … 1.Feature detectionand representation

18. 2. Codewords dictionary formation …

19. 2. Codewords dictionary formation … Vector quantization Slide credit: Josef Sivic

20. 2. Codewords dictionary formation Fei-Fei et al. 2005

21. Image patch examples of codewords Sivic et al. 2005

22. ….. 3. Image representation frequency codewords

23. Representation codewords dictionary feature detection & representation image representation 2. 1. 3.

24. Learning and Recognition codewords dictionary category decision category models (and/or) classifiers

25. Naïve Bayes classifier Csurka et al. 2004 Hierarchical Bayesian text models (pLSA and LDA) Background: Hoffman 2001, Blei et al. 2004 Object categorization: Sivic et al. 2005, Sudderth et al. 2005 Natural scene categorization: Fei-Fei et al. 2005 2 case studies

26. wn: each patch in an image wn = [0,0,…1,…,0,0]T w: a collection of all N patches in an image w = [w1,w2,…,wN] dj: the jth image in an image collection c: category of the image z: theme or topic of the patch First, some notations

27. Case #1: the Naïve Bayes model Object class decision Prior prob. of the object classes Image likelihood given the class c w N Csurka et al. 2004

28. Csurka et al. 2004

29. Csurka et al. 2004

30. Case #2: Hierarchical Bayesian text models z d w N D  z c w N D Probabilistic Latent Semantic Analysis (pLSA) Hoffman, 2001 Latent Dirichlet Allocation (LDA) Blei et al., 2001

31. Case #2: Hierarchical Bayesian text models z d w N D “face” Probabilistic Latent Semantic Analysis (pLSA) Sivic et al. ICCV 2005

32. Case #2: Hierarchical Bayesian text models “beach”  z c w N D Latent Dirichlet Allocation (LDA) Fei-Fei et al. ICCV 2005

33. z d w N D Case #2: the pLSA model

34. z d w N D Observed codeword distributions Theme distributions per image Codeword distributions per theme (topic) Case #2: the pLSA model Slide credit: Josef Sivic

35. Case #2: Recognition using pLSA Slide credit: Josef Sivic

36. Case #2: Learning the pLSA parameters Observed counts of word i in document j Maximize likelihood of data using EM M … number of codewords N … number of images Slide credit: Josef Sivic

37. task: face detection – no labeling

38. Demo: feature detection • Output of crude feature detector • Find edges • Draw points randomly from edge set • Draw from uniform distribution to get scale

39. Demo: learnt parameters • Learning the model: do_plsa(‘config_file_1’) • Evaluate and visualize the model: do_plsa_evaluation(‘config_file_1’) Codeword distributions per theme (topic) Theme distributions per image

40. Demo: recognition examples

41. Learning and Recognition codewords dictionary category decision category models (and/or) classifiers

42. Scale and rotation Implicit Detectors and descriptors Invariance issues Kadir and Brady. 2003

43. Scale and rotation Occlusion Implicit in the models Codeword distribution: small variations (In theory) Theme (z) distribution: different occlusion patterns Invariance issues

44. Scale and rotation Occlusion Translation Encode (relative) location information Invariance issues Sudderth et al. 2005

45. Scale and rotation Occlusion Translation View point (in theory) Codewords: detector and descriptor Theme distributions: different view points Invariance issues Fergus et al. 2005

46. Intuitive Analogy to documents Of all the sensory impressions proceeding to the brain, the visual experiences are the dominant ones. Our perception of the world around us is based essentially on the messages that reach the brain from our eyes. For a long time it was thought that the retinal image was transmitted point by point to visual centers in the brain; the cerebral cortex was a movie screen, so to speak, upon which the image in the eye was projected. Through the discoveries of Hubel and Wiesel we now know that behind the origin of the visual perception in the brain there is a considerably more complicated course of events. By following the visual impulses along their path to the various cell layers of the optical cortex, Hubel and Wiesel have been able to demonstrate that the message about the image falling on the retina undergoes a step-wise analysis in a system of nerve cells stored in columns. In this system each cell has its specific function and is responsible for a specific detail in the pattern of the retinal image. sensory, brain, visual, perception, retinal, cerebral cortex, eye, cell, optical nerve, image Hubel, Wiesel Model properties

47. Intuitive (Could use) generative models Convenient for weakly- or un-supervised training Prior information Hierarchical Bayesian framework Model properties Sivic et al., 2005, Sudderth et al., 2005

48. Intuitive (Could use) generative models Learning and recognition relatively fast Compare to other methods Model properties

49. No rigorous geometric information of the object components It’s intuitive to most of us that objects are made of parts – no such information Not extensively tested yet for View point invariance Scale invariance Segmentation and localization unclear Weakness of the model