2. 2002.09.17 - SLIDE 1IS 202 – FALL 2002 Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30 am - 12:00 am Fall 2002 http://www.sims.berkeley.edu/academics/courses/is202/f02/ SIMS 202: Information Organization and Retrieval Lecture 08: Media Streams

3. 2002.09.17 - SLIDE 2IS 202 – FALL 2002 Lecture 08: Media Streams Problem Setting Current Approaches Representing Media New Solutions Methodological Considerations Future Work

4. 2002.09.17 - SLIDE 3IS 202 – FALL 2002 Lecture 08: Media Streams Problem Setting Current Approaches Representing Media New Solutions Methodological Considerations Future Work

5. 2002.09.17 - SLIDE 4IS 202 – FALL 2002 What is the Problem? Today people cannot easily create, find, edit, share, and reuse media Computers don’t understand media content –Media is opaque and data rich –We lack structured representations Without content representation (metadata), manipulating digital media will remain like word- processing with bitmaps

6. 2002.09.17 - SLIDE 5IS 202 – FALL 2002 Lecture 08: Media Streams Problem Setting Current Approaches Representing Media New Solutions Methodological Considerations Future Work

7. 2002.09.17 - SLIDE 6IS 202 – FALL 2002 The Search for Solutions Current approaches to creating metadata don’t work –Signal-based analysis –Keywords –Natural language Need standardized metadata framework –Designed for video and rich media data –Human and machine readable and writable –Standardized and scaleable –Integrated into media capture, archiving, editing, distribution, and reuse

8. 2002.09.17 - SLIDE 7IS 202 – FALL 2002 Signal-Based Parsing Practical problem –Parsing unstructured, unknown video is very, very hard Theoretical problem –Mismatch between percepts and concepts

9. 2002.09.17 - SLIDE 8IS 202 – FALL 2002 Why Keywords Don’t Work Are not a semantic representation Do not describe relations between descriptors Do not describe temporal structure Do not converge Do not scale

10. 2002.09.17 - SLIDE 9IS 202 – FALL 2002 Jack, an adult male police officer, while walking to the left, starts waving with his left arm, and then has a puzzled look on his face as he turns his head to the right; he then drops his facial expression and stops turning his head, immediately looks up, and then stops looking up after he stops waving but before he stops walking. Natural Language vs. Visual Language

11. 2002.09.17 - SLIDE 10IS 202 – FALL 2002 Natural Language vs. Visual Language Jack, an adult male police officer, while walking to the left, starts waving with his left arm, and then has a puzzled look on his face as he turns his head to the right; he then drops his facial expression and stops turning his head, immediately looks up, and then stops looking up after he stops waving but before he stops walking.

12. 2002.09.17 - SLIDE 11IS 202 – FALL 2002 Notation for Time-Based Media: Music

13. 2002.09.17 - SLIDE 12IS 202 – FALL 2002 Visual Language Advantages A language designed as an accurate and readable representation of time-based media –For video, especially important for actions, expressions, and spatial relations Enables Gestalt view and quick recognition of descriptors due to designed visual similarities Supports global use of annotations

14. 2002.09.17 - SLIDE 13IS 202 – FALL 2002 Lecture 08: Media Streams Problem Setting Current Approaches Representing Media New Solutions Methodological Considerations Future Work

15. 2002.09.17 - SLIDE 14IS 202 – FALL 2002 Representing Video Streams vs. Clips Video syntax and semantics Ontological issues in video representation

16. 2002.09.17 - SLIDE 15IS 202 – FALL 2002 Video is Temporal

17. 2002.09.17 - SLIDE 16IS 202 – FALL 2002 Streams vs. Clips

18. 2002.09.17 - SLIDE 17IS 202 – FALL 2002 Stream-Based Representation Makes annotation pay off –The richer the annotation, the more numerous the possible segmentations of the video stream Clips –Change from being fixed segmentations of the video stream, to being the results of retrieval queries based on annotations of the video stream Annotations –Create representations which make clips, not representations of clips

19. 2002.09.17 - SLIDE 18IS 202 – FALL 2002 Video Syntax and Semantics The Kuleshov Effect Video has a dual semantics –Sequence-independent invariant semantics of shots –Sequence-dependent variable semantics of shots

20. 2002.09.17 - SLIDE 19IS 202 – FALL 2002 Ontological Issues for Video Video plays with rules for identity and continuity –Space –Time –Character –Action

21. 2002.09.17 - SLIDE 20IS 202 – FALL 2002 Space and Time: Actual vs. Inferable Actual Recorded Space and Time –GPS –Studio space and time Inferable Space and Time –Establishing shots –Cues and clues

22. 2002.09.17 - SLIDE 21IS 202 – FALL 2002 Time: Temporal Durations Story (Fabula) Duration –Example: Brushing teeth in story world (5 minutes) Plot (Syuzhet) Duration –Example: Brushing teeth in plot world (1 minute: 6 steps of 10 seconds each) Screen Duration –Example: Brushing teeth (10 seconds: 2 shots of 5 seconds each)

23. 2002.09.17 - SLIDE 22IS 202 – FALL 2002 Character and Continuity Identity of character is constructed through –Continuity of actor –Continuity of role Alternative continuities –Continuity of actor only –Continuity of role only

24. 2002.09.17 - SLIDE 23IS 202 – FALL 2002 Representing Action Physically-based description for sequence-independent action semantics –Abstract vs. conventionalized descriptions –Temporally and spatially decomposable actions and subactions Issues in describing sequence-dependent action semantics –Mental states (emotions vs. expressions) –Cultural differences (e.g., bowing vs. greeting)

25. 2002.09.17 - SLIDE 24IS 202 – FALL 2002 “Cinematic” Actions Cinematic actions support the basic narrative structure of cinema –Reactions/Proactions Nodding, screaming, laughing, etc. –Focus of Attention Gazing, headturning, pointing, etc. –Locomotion Walking, running, etc. Cinematic actions can occur Within the frame/shot boundary Across the frame boundary Across shot boundaries

26. 2002.09.17 - SLIDE 25IS 202 – FALL 2002 Lecture 08: Media Streams Problem Setting Current Approaches Representing Media New Solutions Methodological Considerations Future Work

27. 2002.09.17 - SLIDE 26IS 202 – FALL 2002 New Solutions for Creating Metadata After CaptureDuring Capture

28. 2002.09.17 - SLIDE 27IS 202 – FALL 2002 After Capture: Media Streams

29. 2002.09.17 - SLIDE 28IS 202 – FALL 2002 Media Streams Features Key features –Stream-based representation (better segmentation) –Semantic indexing (what things are similar to) –Relational indexing (who is doing what to whom) –Temporal indexing (when things happen) –Iconic interface (designed visual language) –Universal annotation (standardized markup schema) Key benefits –More accurate annotation and retrieval –Global usability and standardization –Reuse of rich media according to content and structure

30. 2002.09.17 - SLIDE 29IS 202 – FALL 2002 Media Streams GUI Components Media Time Line Icon Space –Icon Workshop –Icon Palette

31. 2002.09.17 - SLIDE 30IS 202 – FALL 2002 Media Time Line Visualize video at multiple time scales Write and read multi-layered iconic annotations One interface for annotation, query, and composition

32. 2002.09.17 - SLIDE 31IS 202 – FALL 2002 Media Time Line

33. 2002.09.17 - SLIDE 32IS 202 – FALL 2002 Icon Space Icon Workshop –Utilize categories of video representation –Create iconic descriptors by compounding iconic primitives –Extend set of iconic descriptors Icon Palette –Dynamically group related sets of iconic descriptors –Reuse descriptive effort of others –View and use query results

34. 2002.09.17 - SLIDE 33IS 202 – FALL 2002 Icon Space

35. 2002.09.17 - SLIDE 34IS 202 – FALL 2002 Icon Space: Icon Workshop General to specific (horizontal) –Cascading hierarchy of icons with increasing specificity on subordinate levels Combinatorial (vertical) –Compounding of hierarchically organized icons across multiple axes of description

36. 2002.09.17 - SLIDE 35IS 202 – FALL 2002 Icon Space: Icon Workshop Detail

37. 2002.09.17 - SLIDE 36IS 202 – FALL 2002 Icon Space: Icon Palette Dynamically group related sets of iconic descriptors Collect icon sentences Reuse descriptive effort of others

38. 2002.09.17 - SLIDE 37IS 202 – FALL 2002 Icon Space: Icon Palette Detail

39. 2002.09.17 - SLIDE 38IS 202 – FALL 2002 Video Retrieval In Media Streams Same interface for annotation and retrieval Assembles responses to queries as well as finds them Query responses use semantics to degrade gracefully

40. 2002.09.17 - SLIDE 39IS 202 – FALL 2002 Media Streams Technologies Minimal video representation distinguishing syntax and semantics Iconic visual language for annotating and retrieving video content Retrieval-by-composition methods for repurposing video

41. 2002.09.17 - SLIDE 40IS 202 – FALL 2002 New Solutions for Creating Metadata After CaptureDuring Capture

42. 2002.09.17 - SLIDE 41IS 202 – FALL 2002 Creating Metadata During Capture New Capture Paradigm 1 Good Capture Drives Multiple Uses Current Capture Paradigm Multiple Captures To Get 1 Good Capture

43. 2002.09.17 - SLIDE 42IS 202 – FALL 2002 Active Capture Active engagement and communication among the capture device, agent(s), and the environment Re-envision capture as a control system with feedback Use multiple data sources and communication to simplify the capture scenario Use HCI to support “human- in-the-loop” algorithms for computer vision and audition

44. 2002.09.17 - SLIDE 43IS 202 – FALL 2002 Active Capture Processing CaptureInteraction Active Capture Computer Vision HCI Direction/ Cinematography

45. 2002.09.17 - SLIDE 44IS 202 – FALL 2002 Automated Capture: Good Capture

46. 2002.09.17 - SLIDE 45IS 202 – FALL 2002 Automated Capture: Error Handling

47. 2002.09.17 - SLIDE 46IS 202 – FALL 2002 Evolution of Media Production Customized production –Skilled creation of one media product Mass production –Automatic replication of one media product Mass customization –Skilled creation of adaptive media templates –Automatic production of customized media

48. 2002.09.17 - SLIDE 47IS 202 – FALL 2002 Movies change from being static data to programs Shots are inputs to a program that computes new media based on content representation and functional dependency (US Patents 6,243,087 & 5,969,716) Central Idea: Movies as Programs Parser Producer Media Content Representation Content Representation

49. 2002.09.17 - SLIDE 48IS 202 – FALL 2002 Jim Lanahan in an MCI Ad

50. 2002.09.17 - SLIDE 49IS 202 – FALL 2002 Jim Lanahan in an @Home Banner

51. 2002.09.17 - SLIDE 50IS 202 – FALL 2002 Automated Media Production Process 2 Annotation and Retrieval Asset Retrieval and Reuse Web Integration and Streaming Media Services Flash Generator WAP HTML Email Print/Physical Media Automated Capture 1 Automatic Editing 3 Personalized Delivery 4 Annotation of Media Assets Reusable Online Asset Database Adaptive Media Engine

52. 2002.09.17 - SLIDE 51IS 202 – FALL 2002 Proposed Technology Architecture Media Processing DB Analysis Engine Interaction Engine Adaptive Media Engine Annotation and Retrieval Engine (MPEG 7) Delivery Engine OS Media Capture File AV Out Network Device Control

53. 2002.09.17 - SLIDE 52IS 202 – FALL 2002 Lecture 08: Media Streams Problem Setting Representing Media Current Approaches New Solutions Methodological Considerations Future Work

54. 2002.09.17 - SLIDE 53IS 202 – FALL 2002 Non-Technical Challenges Standardization of media metadata (MPEG-7) Broadband infrastructure and deployment Intellectual property and economic models for sharing and reuse of media assets

55. 2002.09.17 - SLIDE 54IS 202 – FALL 2002 Technical Research Challenges Develop end-to-end metadata system for automated media capture, processing, management, and reuse Creating metadata –Represent action sequences and higher level narrative structures –Integrate legacy metadata (keywords, natural language) –Gather more and better metadata at the point of capture (develop metadata cameras) –Develop “human-in-the-loop” indexing algorithms and interfaces Using metadata –Develop media components (MediaLego) –Integrate linguistic and other query interfaces

56. 2002.09.17 - SLIDE 55IS 202 – FALL 2002 For More Info Marc Davis Web Site –www.sims.berkeley.edu/~marc Spring 2003 course on “Multimedia Information” at SIMS URAP and GSR positions TidalWave II “New Media” program

57. 2002.09.17 - SLIDE 56IS 202 – FALL 2002 Next Time Metadata for Motion Pictures: MPEG-7 (MED) Readings for next time (in Protected) –“MPEG-7: The Generic Multimedia Content Description Interface, Part 1” (J. M. Martinez, R. Koenen, F. Pereira) –“MPEG-7: Overview of MPEG-7 Description Tools, Part 2” (J. Martinez)

58. 2002.09.17 - SLIDE 57IS 202 – FALL 2002 Homework (!) Assignment 4: Revision of Photo Metadata Design and Project Presentation –Due by Monday, September 23 Completed (Revised) Photo Classifications and Annotated Photos –[groupname]_classification.xls file –[groupname]_photos.xls file –Due by Thursday, September 26 Group Presentation –2 minutes: Presentation of application idea –6 minutes: Presentation of classification and photo browser –2 minutes: residual time for completing explanations and Q + A Photo Browser Page (will be sent to you)