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Carnegie Mellon Univ. Dept. of Computer Science 15-415 - Database Applications

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Carnegie Mellon Univ. Dept. of Computer Science 15-415 - Database Applications. C. Faloutsos Multimedia Indexing. General Overview. Relational model – SQL; db design Indexing; Q-opt;Transaction processing Advanced topics Distributed Databases RAID Authorization / Stat. DB

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Carnegie Mellon Univ. Dept. of Computer Science 15-415 - Database Applications
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  1. Carnegie Mellon Univ.Dept. of Computer Science15-415 - Database Applications C. Faloutsos Multimedia Indexing

  2. General Overview • Relational model – SQL; db design • Indexing; Q-opt;Transaction processing • Advanced topics • Distributed Databases • RAID • Authorization / Stat. DB • Spatial Access Methods • Multimedia Indexing 15-415 - C. Faloutsos

  3. Multimedia - Detailed outline • multimedia • Motivation / problem definition • Main idea / time sequences • images • sub-pattern matching • automatic feature extraction / FastMap 15-415 - C. Faloutsos

  4. Problem Given a large collection of (multimedia) records (eg. stocks) Allow fast, similarity queries 15-415 - C. Faloutsos

  5. Applications • time series: financial, marketing (click-streams!), ECGs, sound; • images: medicine, digital libraries, education, art • higher-d signals: scientific db (eg., astrophysics), medicine (MRI scans), entertainment (video) 15-415 - C. Faloutsos

  6. Sample queries • find medical cases similar to Smith's • Find pairs of stocks that move in sync • Find pairs of documents that are similar (plagiarism?) • find faces similar to ‘Tiger Woods’ 15-415 - C. Faloutsos

  7. Detailed problem defn.: Problem: • given a set of multimedia objects, • find the ones similar to a desirable query object • for example: 15-415 - C. Faloutsos

  8. $price $price $price 1 1 1 365 365 365 day day day distance function: by expert (eg, Euclidean distance) 15-415 - C. Faloutsos

  9. Types of queries • whole match vs sub-pattern match • range query vs nearest neighbors • all-pairs query 15-415 - C. Faloutsos

  10. Design goals • Fast (faster than seq. scan) • ‘correct’ (ie., no false alarms; no false dismissals) 15-415 - C. Faloutsos

  11. Multimedia - Detailed outline • multimedia • Motivation / problem definition • Main idea / time sequences • images • sub-pattern matching • automatic feature extraction / FastMap 15-415 - C. Faloutsos

  12. $price $price $price 1 1 1 365 365 365 day day day Main idea • Eg., time sequences, ‘whole matching’, range queries, Euclidean distance 15-415 - C. Faloutsos

  13. Main idea • Seq. scanning works - how to do faster? 15-415 - C. Faloutsos

  14. Idea: ‘GEMINI’ (GEneric Multimedia INdexIng) Extract a few numerical features, for a ‘quick and dirty’ test 15-415 - C. Faloutsos

  15. S1 F(S1) 1 365 1 365 F(Sn) day day Sn ‘GEMINI’ - Pictorially eg,. std eg, avg 15-415 - C. Faloutsos

  16. GEMINI Solution: Quick-and-dirty' filter: • extract n features (numbers, eg., avg., etc.) • map into a point in n-d feature space • organize points with off-the-shelf spatial access method (‘SAM’) • discard false alarms 15-415 - C. Faloutsos

  17. GEMINI Important: Q: how to guarantee no false dismissals? A1: preserve distances (but: difficult/impossible) A2: Lower-bounding lemma: if the mapping ‘makes things look closer’, then there are no false dismissals 15-415 - C. Faloutsos

  18. GEMINI Important: Q: how to extract features? A: “if I have only one number to describe my object, what should this be?” 15-415 - C. Faloutsos

  19. Time sequences Q: what features? 15-415 - C. Faloutsos

  20. Time sequences Q: what features? A: Fourier coefficients (we’ll see them in detail soon) 15-415 - C. Faloutsos

  21. Time sequences white noise brown noise Fourier spectrum ... in log-log 15-415 - C. Faloutsos

  22. Time sequences Eg.: 15-415 - C. Faloutsos

  23. Time sequences conclusion: colored noises are well approximated by their first few Fourier coefficients colored noises appear in nature: 15-415 - C. Faloutsos

  24. brown noise: stock prices (1/f2 energy spectrum) pink noise: works of art (1/f spectrum) black noises: water reservoirs (1/fb , b>2) (slope: related to ‘Hurst exponent’, for self-similar traffic, like, eg. Ethernet/web [Schroeder], [Leland+] Time sequences 15-415 - C. Faloutsos

  25. Time sequences - results keep the first 2-3 Fourier coefficients faster than seq. scan NO false dismissals (see book) total time cleanup-time r-tree time # coeff. kept 15-415 - C. Faloutsos

  26. improvements/variations: [Kanellakis+Goldin], [Mendelzon+Rafiei] could use Wavelets, or DCT could use segment averages [Yi+2000] Time sequences - improvements: 15-415 - C. Faloutsos

  27. Multimedia - Detailed outline • multimedia • Motivation / problem definition • Main idea / time sequences • images (color, shapes) • sub-pattern matching • automatic feature extraction / FastMap 15-415 - C. Faloutsos

  28. Images - color what is an image? A: 2-d array 15-415 - C. Faloutsos

  29. Images - color Color histograms, and distance function 15-415 - C. Faloutsos

  30. Images - color Mathematically, the distance function is: 15-415 - C. Faloutsos

  31. Problem: ‘cross-talk’: Features are not orthogonal -> SAMs will not work properly Q: what to do? A: feature-extraction question Images - color 15-415 - C. Faloutsos

  32. possible answers: avg red, avg green, avg blue it turns out that this lower-bounds the histogram distance -> no cross-talk SAMs are applicable Images - color 15-415 - C. Faloutsos

  33. Images - color time performance: seq scan w/ avg RGB selectivity 15-415 - C. Faloutsos

  34. Multimedia - Detailed outline • multimedia • Motivation / problem definition • Main idea / time sequences • images (color; shape) • sub-pattern matching • automatic feature extraction / FastMap 15-415 - C. Faloutsos

  35. distance function: Euclidean, on the area, perimeter, and 20 ‘moments’ (Q: how to normalize them? Images - shapes 15-415 - C. Faloutsos

  36. distance function: Euclidean, on the area, perimeter, and 20 ‘moments’ (Q: how to normalize them? A: divide by standard deviation) Images - shapes 15-415 - C. Faloutsos

  37. distance function: Euclidean, on the area, perimeter, and 20 ‘moments’ (Q: other ‘features’ / distance functions? Images - shapes 15-415 - C. Faloutsos

  38. distance function: Euclidean, on the area, perimeter, and 20 ‘moments’ (Q: other ‘features’ / distance functions? A1: turning angle A2: dilations/erosions A3: ... ) Images - shapes 15-415 - C. Faloutsos

  39. distance function: Euclidean, on the area, perimeter, and 20 ‘moments’ Q: how to do dim. reduction? Images - shapes 15-415 - C. Faloutsos

  40. distance function: Euclidean, on the area, perimeter, and 20 ‘moments’ Q: how to do dim. reduction? A: Karhunen-Loeve (= centered PCA/SVD) Images - shapes 15-415 - C. Faloutsos

  41. Performance: ~10x faster Images - shapes log(# of I/Os) all kept # of features kept 15-415 - C. Faloutsos

  42. Case study: Informedia • Video database system, developed at CMU • 2+ TB of video data (broadcast news) • retrieval by text, image and face similarity www.informedia.cs.cmu.edu/ 15-415 - C. Faloutsos

  43. Case study: Informedia • next foils: visualization features • by space • by time • by concept 15-415 - C. Faloutsos

  44. geo mapping • automatic place recognition • ambiguity resol. + • lookup 15-415 - C. Faloutsos

  45. 15-415 - C. Faloutsos

  46. time line 15-415 - C. Faloutsos

  47. concept space 15-415 - C. Faloutsos

  48. Multimedia - Detailed outline • multimedia • Motivation / problem definition • Main idea / time sequences • images (color; shape) • sub-pattern matching • automatic feature extraction / FastMap 15-415 - C. Faloutsos

  49. Sub-pattern matching • Problem: find sub-sequences that match the given query pattern 15-415 - C. Faloutsos

  50. $price $price 1 400 1 365 day day $price 30 1 300 day 15-415 - C. Faloutsos