From Structural Media Analysis to Exploratory Search

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From Structural Media Analysis to Exploratory Search

  1. 1. Strukturelle und semantische Analyse audiovisueller Medien - von der automatischen Schnitterkennung zur explorativen Suche Potsdam, 21. Januar 2013 Dr. Harald Sack Hasso-Plattner-Institut for IT-Systems Engineering University of PotsdamMontag, 21. Januar 13
  2. 2. Hasso Plattner Institute for IT Systems Engineering Universität Potsdam • HPI was founded in October 1998 as a Public-Private-Partnership • HPI Research and Teaching is focussed on IT Systems Engineering • 10 Professors and 100 Scientific Coworkers • 450 Bachelor / Master Students • HPI is winner of CHE-Ranking http://hpi.uni-potsdam.de/Montag, 21. Januar 13
  3. 3. Hasso Plattner Institute for IT Systems Engineering Semantic Technologies & Multimedia Retrieval Research Group • Research Topics • Semantic Web Technologies • Ontological Engineering • Information Retrieval • Multimedia Analysis & Retrieval • Social Networking • Data/Information Visualization • Research Projects:Montag, 21. Januar 13
  4. 4. Strukturelle und semantische Analyse audiovisueller Medien von der automatischen Schnitterkennung zur explorativen Suche Overview (1) Searching Audiovisual Data (2) Structural Analysis of Audiovisual Data (3) Semantic Multimedia Analysis (4) Explorative Semantic SearchHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011Montag, 21. Januar 13
  5. 5. 5 The ‘Google Dilemma‘ Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  6. 6. 6 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  7. 7. Google Multimedia SearchHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, Workshop ,Corporate Semantic Web‘, XInnovations 2011, Berlin, 19. Sep. 2011Montag, 21. Januar 13
  8. 8. How does Google find Multimedia?Harald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011Montag, 21. Januar 13
  9. 9. Harald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, Workshop ,Corporate Semantic Web‘, XInnovations 2011, Berlin, 19. Sep. 2011Montag, 21. Januar 13
  10. 10. How does Google find Multimedia? ‣Google Multimedia Search relies on metadata and link contextHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, Workshop ,Corporate Semantic Web‘, XInnovations 2011, Berlin, 19. Sep. 2011Montag, 21. Januar 13
  11. 11. How does Google find Multimedia?Harald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, Workshop ,Corporate Semantic Web‘, XInnovations 2011, Berlin, 19. Sep. 2011Montag, 21. Januar 13
  12. 12. How to Search in Multimedia Archives?Harald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011Montag, 21. Januar 13
  13. 13. How to Search in Multimedia Archives? Step 1: Digitalization of analog data Step 2: Annotation with (textbased) metadataHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011Montag, 21. Januar 13
  14. 14. How to Search in Multimedia Archives?• manual anotation with text-based descriptive metadataHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011Montag, 21. Januar 13
  15. 15. Strukturelle und semantische Analyse audiovisueller Medien von der automatischen Schnitterkennung zur explorativen Suche Overview (1) Searching Audiovisual Data (2) Structural Analysis of Audiovisual Data (3) Semantic Multimedia Analysis (4) Explorative Semantic SearchHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011Montag, 21. Januar 13
  16. 16. Automated Audiovisual Analysis Genre Analysis Classification: Studio Indoor overlay News Show Face Logo text Detection Detection scene text Audio-Mining structural Automated speaker analysis Speech identification RecognitionHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011Montag, 21. Januar 13
  17. 17. Structural Video Analysis • Decomposition of time-based media into meaningful media fragments of coherent content that can be used as basic element for indexing and classification video scenes shots subshots framesMontag, 21. Januar 13
  18. 18. Structural Video Analysis • Decomposition of time-based media into meaningful media fragments of coherent content that can be used as basic element for indexing and classification video scenes shots subshots framesMontag, 21. Januar 13
  19. 19. Structural Video Analysis • Decomposition of time-based media into meaningful media fragments of coherent content that can be used as basic element for indexing and classification video scenes shots subshots framesMontag, 21. Januar 13
  20. 20. Structural Video Analysis • Decomposition of time-based media into meaningful media fragments of coherent content that can be used as basic element for indexing and classification video scenes shots subshots framesMontag, 21. Januar 13
  21. 21. Structural Video Analysis • Decomposition of time-based media into meaningful media fragments of coherent content that can be used as basic element for indexing and classification video scenes shots subshots frames keyframesMontag, 21. Januar 13
  22. 22. Structural Video Analysis • Shot Boundary Detection time • Automated Identification of • Hard Cuts • Defects, as e.g., Drop Outs, White Outs, etc. • Soft Cuts, as e.g., Fade-In/Out, Dissolve, Wipe, Cross-Fade, etc. • Analytical Shot Boundary Detection • Based on Luminance / Chrominance / Edge Distribution / Entropy • Adaptive Threshold Computation • Machine Learning Based Shot Detection • Support Vector Machines • Random Forrests ClassifierMontag, 21. Januar 13
  23. 23. Structural Video Analysis • Shot Boundary Detection • Automated Identification of Hard Cuts based on • Luminance/Chrominance Histogram Differences & Derivatives • Edge Distribution/Density 573 574 575 576 577 578Montag, 21. Januar 13
  24. 24. Shot Boundary Detection Adaptive Threshold 1 2 20 1 3 i+W 1 X tha (i) = ↵ · 4@ Da (k, k 1)A Da (i, i 1)5 + k=i W 20 1 i+W 1 X 3 4 4@ 1)A Da(i,i-1) ...D thai = ↵ · th (L2-norm) Da (k, k Frames i (i) Histogram Difference (i) between a (i, 1) > ↵ 20 1 and i-1 of Subregion a k=i W 3 i+W 1 X tha (i) = ↵ · 4@tha(i) ... Da (k, k Threshold for Frameiai (i,1)5 + >a th↵ (i) adaptive 1)A Da (i, of Subregion Decompose Frame into a=4 Subregions Da (i + 1, i) < th↵ (i) D i 1) k=i W Hardcut: if Da (i, i 1) > th↵ (i) and Da (i + 1, i) < th↵ (i) is true for all Subregions a Da (i + 1, i) < th↵ (i) Window Size=4 (W=2) i-3 i-2 i-1 i i+1 i+2Montag, 21. Januar 13
  25. 25. Shot Boundary vs. Defect Analysis • Automated Identification of Defects and Distinction from Shot Boundaries Histogram/Chrominance Difference Analysis Drop Out Flashlight / White Out i i+1 i+8 i+9 i+10 i+11 i+12 i+13 Histogram/Chrominance Difference AnalysisMontag, 21. Januar 13
  26. 26. Shot Boundary Detection • Automated Identification of Soft Cuts, , as e.g. Fade Out / Fade In • Features applied for machine learning: • luminance histogram (Fade In / Fade Out) • luminance average Yµ and luminance variance Yσ2 follow distinct patterns • image decomposition 1 2 3 • component-based analysis to distinguish regional and global changes in image content 4 5 6 • entropy • motion vectors 7 8 9Montag, 21. Januar 13
  27. 27. Shot Boundary Detection • Automated Identification of Soft Cuts, , as e.g. Fade Out / Fade In • Features deployed for machine learning: • luminance/chrominance histogram • entropy • motion vectors 1 2 • image decomposition • compute average motion vectors for all areas • identify camera movements (zoom, pan, etc.) and 3 4 moving objectsMontag, 21. Januar 13
  28. 28. Structural Audio Analysis • Segmentation of Video Data by Audio Event Detection • Pauses / Silence • Monologues (male, female) n, bevor man das ge einen sehr hohen Anteil flacher Übergänge vorauszusetze • Dialogues zeigt, wie sich die vall als flach werte t. Siehe hierzu auch Abbildung 3.4, die s auswirkt. Man e Fehlerwahrscheinlichkeit des Verfahren • Music, Laughter, Applause Schwellweman bedie Werte gleichermaßen ändern muss, um die Fehlerwahrs rte auf ide lich, dass • Traffic Noise, Cheering, Shouting,zu halten. gering etc. • simple analytical methods bysed on • Intensity • Heigth and Variation of    Basic Frequencies                              Montag, 21. Januar 13
  29. 29. Intelligent Character Recognition • Video OCR is much more difficult than traditional print OCR • heterogeneous/low contrast • bad lighting conditions • skewed and distorted text • compression artefacts • etc.Montag, 21. Januar 13
  30. 30. Intelligent Character Recognition • Preprocessing • Character Identification • Text Preprocessing • Text Filtering • Adaption of script geometry (Deskew) • Image Quality Enhancement • Optical Character Recognition (OCR) • Standard OCR software (OCRopus) • Postprocessing Text Filtering • Lexical analysis • Statistical / context based filtering Image Quality Enhancement OCR RostockMontag, 21. Januar 13
  31. 31. Intelligent Character Recognition • Analytical Textbox Filtering horizontal projection • Horizontal & Vertical profile Projection Profile • Stroke Width Analysis Based Verification vertical projection profileMontag, 21. Januar 13
  32. 32. Intelligent Character Recognition • Analytical Textbox Filtering • Horizontal & Vertical Projection Profile • Stroke Width Analysis Based Verification Frame with Candidate Frame with Verified Textboxes TextboxesMontag, 21. Januar 13
  33. 33. Intelligent Character Recognition Analytical Edge Based Character Identificationflow of the proposed text detection method. (b) is the vertical edge map of (a). (c) is the vertical d binary 1. Workflow ofthe result map text detection method. (b) is the vertical edge (f) shows the(c) Fig. map of (c). (e) the proposed of subsequent connected component analysis. map of (a). bprojection profile refinement. (g) is (e) the result map of subsequent connected component analysis. Montag, 21. Januar 13the binary map of (c). (b). (d) is the final detection result.
  34. 34. Intelligent Character Recognition Character Binarization & Normalization Original Video Frames Textbox Textbox Quality NormalizationEnhancement and BinarizationMontag, 21. Januar 13
  35. 35. Intelligent Character Recognition Standard Optical Character Recognition • OCRopus 0.4.4 (Open Source, Apache License v2.0) • Tesseract 3.01 (Open Source, Apache License v2.0) Quality Enhanced Raw OCR Results Normalized Textboxes Ueutsche Bank WeubrandenburgMontag, 21. Januar 13
  36. 36. Video OCR OCR Post Processing • OCR-adapted Spell Correction (hunspell 1.3.2, Open Source GNU lGPL) • exploits temporal redundancies for Spell Correction • exploits context for Spell Correction OCR-adapted OCR Results after Frame Raw OCR Results Spell Correction Spell Correction n Ueutsche Bank n+1 Deutsche Bunk n+2 eutsche Bimk Deutsche Bank n+3 Deutschi Bank n+4 Deutsche BankMontag, 21. Januar 13
  37. 37. Face Detection, Tracking & Clustering • Face Detection Detection of Faces in single frames • Cascade of various filter algorithms • growing precision vs. complexity lbpcscade haarcscade libfaceMontag, 21. Januar 13
  38. 38. Face Detection Tasks • Face Tracking Tracking of a detected face within a scene • probabilistic mapping criteria: • center distance of consecutive bounding boxes • overlapping area of consecutive bounding boxes • size variations of of consecutive bounding boxesMontag, 21. Januar 13
  39. 39. Face Detection Tasks • Bounding Box Extension: • Inclusion of areas above (hair) and below • Face Clustering (clothing) of the original bounding classification box for Clustering of detected faces • Feature Extraction: within a video according to visual similarity • texture based features (eLBP, GVC) • color related features (chrominance histograms) Clustering: • Determine the number of clusters k (via silhouette coefficient optimizatio n) • k-means Clustering / Mean Shift Clustering / Hierarchical Cluster ing Person 3 Person 1 Person 2Montag, 21. Januar 13
  40. 40. Strukturelle und semantische Analyse audiovisueller Medien von der automatischen Schnitterkennung zur explorativen Suche Overview (1) Searching Audiovisual Data (2) Structural Analysis of Audiovisual Data (3) Semantic Multimedia Analysis (4) Explorative Semantic SearchHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011Montag, 21. Januar 13
  41. 41. Annotation of Audiovisual Data • Multimedia data with spatiotemporal Annotations Metadata Extraction Metadata (e.g. MPEG-7) ... <SpatialDecomposition> <TextAnnotation> <KeywordAnnotation> <Keyword>Astronaut</Keyword> </KeywordAnnotation> </TextAnnotation> <SpatialMask> <SubRegion> <Polygon> <Coords> 480 150 620 480 </Coords> </Polygon> </SubRegion> ong </SpatialMask> Neil Armstr ... </SpatialDecomposition> ...Montag, 21. Januar 13
  42. 42. ,Neil Armstrong‘ is more than just a character string Juri Gagarin is a Neil Armstrong Entities is a is a Ontologies same as Kosmonaut Astronaut Person is a is NOT a Science Occupation is a has an EmploymentMontag, 21. Januar 13
  43. 43. Where does the knowledge come from...?Montag, 21. Januar 13
  44. 44. Where does the knowledge come from...?Montag, 21. Januar 13
  45. 45. Web of Data Neil Armstrong is a is a Astronaut Person is a Science Occupation is a has a EmploymentMontag, 21. Januar 13
  46. 46. Semantic Analysis Named Entity Recognition „Armstrong was the first man on the Moon.“ Text Entity Mapping Neil Armstrong is a is a Astronaut Person subClassOf Science Occupation subClassOf EmploymentMontag, 21. Januar 13
  47. 47. Semantic Analysis Named Entity Recognition Text „Armstrong was the first man on the Moon.“ Text Determine possible Entity Mapping Candidates Anton Armstrong Armstrong, Ontario Armstrong Tools Ian Armstrong Armstrong, Florida Armstrong (car) Edward Armstrong How do I Armstrong (moon crater) Armstrong County, Texas find the r Gary Armstrong The Armstrongs igh George Armstrong t Armstrong Tunnel entity? Louis Armstrong The Armstrong Twins Craig Armstrong + 200 more...Montag, 21. Januar 13
  48. 48. Semantic Analysis Semantic Analysis Named EntityContext Dimensions for Audiovisual Media Recognition Spatial Temporal Context Context Provenance User Context Context Context provides information for Structural Context • Disambiguation • Reliability • TrustworthinessMontag, 21. Januar 13
  49. 49. Semantic Analysis Named Entity Recognition Armstrong Eagle Moon 448 entities 95 entities 156 entities Man on the Moon (film) George Armstrong Custer Eagle (Bird) Moon (song) Neil Armstrong Eagle (heraldry) Moon Son-Ri The Armstrong Twins USCGC Eagle Moon 44 C Moon Armstrong, Florida The Eagle (2011 film) Eagle (comic) The Moon (Tarot card) Craig Armstrong Armstrong, Ontario Man on the Moon (soundtrack) Eagle (song) Moon Armstrong (Moon Crater) Eagle (lunar module) Armstrong Gun The Eagle (newspaper) Man on the Moon (musical) Armstrong‘s Theorem War Eagle Mr. Moon (song) Eagle (Moon Crater) Louis Armstrong International Airport Moon (Band) The Eagle (Pub) Armstrong County, Texass Moon OS Eagle TV Eagle Falls (Washington) Moon 83 Joe Armstrong Lottie Moon Ian Armstrong Eagle (racehorse) Edgar Moon Armstrong Tunnel Armstrong Tunnel Armstrong Automobile John H. Eagle Darvin Moon Sir Thomas Armstrong Eagle (typeface) Gary Moon William Moon Louis Armstrong Angela Eagle Francis Moon Armstrong (British Columbia) Linda Eagle Robert Charles Moon Karen Armstrong Allan Moon Curtis Armstrong James Philipp Eagle Fly me to the Moon (song) Hilary Armstrong Black Moon Ban-Ki Moon Gillian Armstrong William L. ArmstrongMontag, 21. Januar 13
  50. 50. Semantic Analysis Named Entity Recognition • Popularity based Strategies • Linguistical Strategies • Statistical Strategies • Semantic based Strategies General Approach 1. Make an assumption 2. Do the strategies support or contradict your assumption 3. Make decision according to logical and probabilistic rulesMontag, 21. Januar 13
  51. 51. Semantic Analysis Named Entity Recognition Armstrong Eagle Moon 448 entities 95 entities 156 entities Man on the Moon (film) George Armstrong Custer Eagle (Bird) Moon (song) Neil Armstrong Eagle (heraldry) Moon Son-Ri The Armstrong Twins USCGC Eagle Moon 44 C Moon Armstrong, Florida The Eagle (2011 film) Eagle (comic) The Moon (Tarot card) Craig Armstrong Armstrong, Ontario Moon Man on the Moon (soundtrack) Eagle (song) Armstrong (Moon Crater) Eagle (lunar module) Armstrong Gun The Eagle (newspaper) Man on the Moon (musical) Armstrong‘s Theorem War Eagle Mr. Moon (song) Eagle (Moon Crater) Louis Armstrong International Airport Moon (Band) The Eagle (Pub) Armstrong County, Texass Moon OS Eagle TV Eagle Falls (Washington) Moon 83 Joe Armstrong Lottie Moon Ian Armstrong Eagle (racehorse) Edgar Moon Armstrong Tunnel Armstrong Tunnel Armstrong Automobile John H. Eagle Darvin Moon Sir Thomas Armstrong Eagle (typeface) Gary Moon William Moon Louis Armstrong Angela Eagle Francis Moon Armstrong (British Columbia) Linda Eagle Robert Charles Moon Karen Armstrong Allan Moon Curtis Armstrong James Philipp Eagle Fly me to the Moon (song) Hilary Armstrong Black Moon Ban-Ki Moon Gillian Armstrong William L. ArmstrongMontag, 21. Januar 13
  52. 52. Semantic Multimedia Analysis Video Analysis / Metadata Extraction metadata metadata metadata metadata metadata Entity Recognition Entity Mapping e.g., bibliographical data, geographical data, encyclopedic data, ..Montag, 21. Januar 13
  53. 53. Strukturelle und semantische Analyse audiovisueller Medien von der automatischen Schnitterkennung zur explorativen Suche Overview (1) Searching Audiovisual Data (2) Structural Analysis of Audiovisual Data (3) Semantic Multimedia Analysis (4) Explorative Semantic SearchHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011Montag, 21. Januar 13
  54. 54. Entity Based Search54• Query string refinement / extension • linguistic ambiguities of traditional keyword based• entity auto-suggestion search can be avoided• interpretation of natural language queries • enables high precision and high recall retrieval http://www.yovisto.com/labs/autosuggestion/ Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  55. 55. Semantic Search Entity Based Search search facetsMontag, 21. Januar 13
  56. 56. Searching is not56 always just searching Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  57. 57. I‘m looking for the book „Brave New World“ by Aldous Huxley in the first German edition...57 Brave Ne - The Al w World. (Hamburg batros C - Aldous H U X ontinent L E Y. 257 S. 8 usw., Albatros al Library, 47 “ Verlag, 1933) II 1, 25 06, 3454 8 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  58. 58. 58 I really liked „Brave New World“ by Aldous Huxley but how should I find what to read next...? Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  59. 59. 59 Exploratory Search • What, if the user does not know, which query string to use? • What, if the user is looking for complex answers ? • What, if the user does not know the domain he/she is looking for? • What, if the user wants to know all(!) about a specific topic? • ...,Browsing‘ instead of ,Searching‘ • ...to find something by chance, i.e. Serendipity • ...to get an overview • ...enable content based navigation Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  60. 60. Enable Exploratory Search based on Linked Open Data60 http://dbpedia.org/page/Brave_New_World Gather knowledge about dbpedia:Brave_New_World and decide, which intersting fact to follow.... Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  61. 61. 61 or uth :a wl r -o ho ut dia l :a w pe ia-o ed db p db dbpedia-owl:author dbpedia-owl:author dbpedia:Aldous_Huxley dbpedia:Brave_New_World Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  62. 62. dbpedia:H._G._Wells62 dbpedia:George_Orwell es nc lue inf y/ log to s ce on en flu ia: in y/ ed g p olo nt db ia:o p ed db dbpedia-owl:author dbpedia:ontology/influences dbpedia:Aldous_Huxleydbpedia:Brave_New_World dbpedia:Michel_Houellebecq Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  63. 63. dbpedia:H._G._Wells dbpedia:George_Orwell dbpedia:Michel_Houellebecq63 dbpedia-owl:notableWork dbpedia-owl:notableWork dbpedia-owl:notableWorkdbpedia:The_Time_Machine dbpedia:Nineteen_Eighty-Four dbpedia:Les_Particules_élémentaires Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  64. 64. ...and now please surprise me.....SERENDIPITY64 Yago:EnglishExpatriatesInTheUnitedStates dbpedia:Tim_Berners-Lee dbpedia-owl:author rdf:type rdf:type rdf:type dbpedia-owl:starring dbpprop:inventordbpedia:Aldous_Huxley dbpedia:Patrick_Stewart dbpedia:World_Wide_Web dbpedia:Star_Trek:_The_Next_Generation Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  65. 65. Explorative Search dbpedia:Michael_Collins65 dbpedia-owl:mission dbpedia:Apollo_11 dbpedia-owl:mission dcterms:subject dbpedia-owl:mission dbpedia:Neil_Armstrong dbpedia:Buzz_Collins dcterms:subject category:Apollo_program dbpedia:Apollo_13 rdf:type dbpedia:Space_Shuttle_Challenger yago:Space_accidents_and_incidents rdf:type Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  66. 66. Exploratory Search and Serendipity • Find something that you were not looking for on purpose ... dbpedia:Buzz_Collins dbpedia:Cookie_Monster dbpedia:Strictly_Come_DancingMontag, 21. Januar 13
  67. 67. Exploratory Search with yovisto67 http://mediaglobe.yovisto.com:8080/ Waitelonis, Sack: Augmenting Video Search with Linked Open Data, Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam in Proc. I-Semantics , Graz 2009.Montag, 21. Januar 13
  68. 68. 68http://mediaglobe.yovisto.com:8080/mggui/#start Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamMontag, 21. Januar 13
  69. 69. Strukturelle und semantische Analyse audiovisueller Medien von der automatischen Schnitterkennung zur explorativen Suche Overview (1) Searching Audiovisual Data (2) Structural Analysis of Audiovisual Data (3) Semantic Multimedia Analysis (4) Explorative Semantic SearchHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011Montag, 21. Januar 13
  70. 70. Contact: Dr. Harald Sack Hasso-Plattner-Institut für Softwaresystemtechnik Universität Potsdam Prof.-Dr.-Helmert-Str. 2-3 D-14482 Potsdam Homepage: ttp://www.hpi.uni-potsdam.de/meinel/team/sack.html h http://www.yovisto.com/ Blog: http://yovisto.blogspot.com/ E-Mail: harald.sack@hpi.uni-potsdam.de Twitter: lysander07 / biblionomicon / yovisto mu ch ve ry k y ou T han tio n! ur at ten f or yoHarald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011Montag, 21. Januar 13

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