Part 1: Bag-of-words models

1. Part 1: Bag-of-words models by Li Fei-Fei (UIUC)

2. Related works • 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

3. Object Bag of ‘words’

4. 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 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.

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

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

8. 1.Feature detection and representation

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

10. 1.Feature detection and representation • 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

11. 1.Feature detection and representation • 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)

12. 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

13. … 1.Feature detectionand representation

14. … 2. Codewords dictionary formation

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

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

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

18. ….. 3. Image representation frequency codewords

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

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

21. 2 case studies • 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

22. First, some notations • 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

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

24. Csurka et al. 2004

25. Csurka et al. 2004

26. 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

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

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

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

30. 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

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

32. 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

33. Demo • Course website

34. task: face detection – no labeling

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

36. 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

37. Demo: recognition examples

38. Demo: categorization results • Performance of each theme

39. Demo: naïve Bayes • Learning the model: do_naive_bayes(‘config_file_2’) • Evaluate and visualize the model: do_naive_bayes_evaluation(‘config_file_2’)

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

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

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

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

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

45. 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

46. 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

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

48. Weakness of the model • 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