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Agenda

Agenda. Introduction Bag-of-words model Visual words with spatial location Part-based models Discriminative methods Segmentation and recognition Recognition-based image retrieval Datasets & Conclusions. Object. Bag of ‘words’.

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Agenda

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  1. Agenda • Introduction • Bag-of-words model • Visual words with spatial location • Part-based models • Discriminative methods • Segmentation and recognition • Recognition-based image retrieval • Datasets & Conclusions

  2. Object Bag of ‘words’

  3. 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.

  4. A clarification: definition of “BoW” • Independent features • Histogram representation of image • Discrete appearance representation

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

  6. 1.Feature detection and representation

  7. 1.Feature detection and representation • Regular grid • Vogel & Schiele, 2003 • Fei-Fei & Perona, 2005

  8. 1.Feature detection and representation • Regular grid • Vogel & Schiele, 2003 • Fei-Fei & Perona, 2005 • Interest point detector • Csurka, et al. 2004 • Fei-Fei & Perona, 2005 • Sivic, et al. 2005

  9. 1.Feature detectionand representation Compute SIFT descriptor [Lowe’99] Normalize patch Detect patches [Mikojaczyk and Schmid ’02] [Mata, Chum, Urban & Pajdla, ’02] [Sivic & Zisserman, ’03] Slide credit: Josef Sivic

  10. … 1.Feature detectionand representation

  11. … 2. Codewords dictionary formation

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

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

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

  15. ….. 3. Image representation frequency codewords

  16. Representation codewords dictionary feature detection & representation image representation 2. 1. 3. category models (and/or) classifiers

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

  18. Learning and Recognition • Generative method: • - topic models • Discriminative method: • - SVM category models (and/or) classifiers

  19. Probabilistic Latent Semantic Analysis (pLSA) • Background: Hoffman, 2001Blei, Ng & Jordan, 2004  Latent Dirichlet Allocation • Object categorization: Sivic et al. 2005Sudderth et al. 2005 • Natural scene categorization: Fei-Fei et al. 2005 In this case, use it for unsupervised learningfrom image collections

  20. Probabilistic Latent Semantic Analysis z d w N D “face” dj: the jth image in an image collection z: latent theme or topic of the patch N: number of patches per image wi: visual word of patch Sivic et al. ICCV 2005

  21. z d w N D Feature detection and representation Image collection d w P(wi|dj)

  22. z d w N D Observed codeword distributions Theme distributions per image Codeword distributions per theme (topic) The pLSA model Slide credit: Josef Sivic

  23. 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

  24. Recognition using pLSA Slide credit: Josef Sivic

  25. Demo • Course website

  26. task: face detection – no labeling

  27. 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

  28. Demo: recognition examples

  29. Learning and Recognition • Generative method: • - topic models • Discriminative method: • - SVM category models (and/or) classifiers

  30. Discriminative methods based on ‘bag of words’ representation • Grauman & Darrell, 2005, 2006: • SVM w/ Pyramid Match kernels • Others • Csurka, Bray, Dance & Fan, 2004 • Serre & Poggio, 2005

  31. Summary: Pyramid match kernel optimal partial matching between sets of features • Pyramid is in feature space, spatial information not used • Efficient to compute – linear in # features/image • Satisfies Mercer Condition, so can be used as a kernel in an SVM Grauman & Darrell, 2005, Slide credit: Kristen Grauman

  32. Pyramid Match (Grauman & Darrell 2005) Histogram intersection Slide credit: Kristen Grauman

  33. matches at this level matches at previous level Difference in histogram intersections across levels counts number ofnew pairs matched Pyramid Match (Grauman & Darrell 2005) Histogram intersection Slide credit: Kristen Grauman

  34. histogram pyramids number of newly matched pairs at level i measure of difficulty of a match at level i Pyramid match kernel • Weights inversely proportional to bin size • Normalize kernel values to avoid favoring large sets Slide credit: Kristen Grauman

  35. Example pyramid match Level 0 Slide credit: Kristen Grauman

  36. Example pyramid match Level 1 Slide credit: Kristen Grauman

  37. Example pyramid match Level 2 Slide credit: Kristen Grauman

  38. Example pyramid match pyramid match optimal match Slide credit: Kristen Grauman

  39. Object recognition results • ETH-80 database 8 object classes (Eichhorn and Chapelle 2004) • Features: • Harris detector • PCA-SIFT descriptor, d=10 d = descriptor dim. ; m = # features ; L = # levels in pyramid Slide credit: Kristen Grauman

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