Metadata for Motion Pictures: Media Streams
Transcript of Metadata for Motion Pictures: Media Streams
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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 2002http://www.sims.berkeley.edu/academics/courses/is202/f02/
SIMS 202:
Information Organization
and Retrieval
Lecture 08: Media Streams
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2002.09.17 - SLIDE 2IS 202 – FALL 2002
Lecture 08: Media Streams
• Problem Setting
• Current Approaches
• Representing Media
• New Solutions
• Methodological Considerations
• Future Work
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2002.09.17 - SLIDE 3IS 202 – FALL 2002
Lecture 08: Media Streams
• Problem Setting
• Current Approaches
• Representing Media
• New Solutions
• Methodological Considerations
• Future Work
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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
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2002.09.17 - SLIDE 5IS 202 – FALL 2002
Lecture 08: Media Streams
• Problem Setting
• Current Approaches
• Representing Media
• New Solutions
• Methodological Considerations
• Future Work
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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
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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
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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
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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
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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.
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2002.09.17 - SLIDE 11IS 202 – FALL 2002
Notation for Time-Based Media: Music
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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
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2002.09.17 - SLIDE 13IS 202 – FALL 2002
Lecture 08: Media Streams
• Problem Setting
• Current Approaches
• Representing Media
• New Solutions
• Methodological Considerations
• Future Work
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2002.09.17 - SLIDE 14IS 202 – FALL 2002
Representing Video
• Streams vs. Clips
• Video syntax and semantics
• Ontological issues in video representation
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2002.09.17 - SLIDE 15IS 202 – FALL 2002
Video is Temporal
Stream of 100 Frames of Video
A Clip from Frame 47 to Frame 68 with Descriptors
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2002.09.17 - SLIDE 16IS 202 – FALL 2002
Streams vs. Clips
The Stream of 100 Frames of Video with 6 Annotations Resulting in ManyPossible Segmentations of the Stream
Stream of 100 Frames of Video
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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
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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
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2002.09.17 - SLIDE 19IS 202 – FALL 2002
Ontological Issues for Video
• Video plays with rules for identity and continuity
– Space
– Time
– Character
– Action
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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
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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)
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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
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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)
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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
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2002.09.17 - SLIDE 25IS 202 – FALL 2002
Lecture 08: Media Streams
• Problem Setting
• Current Approaches
• Representing Media
• New Solutions
• Methodological Considerations
• Future Work
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2002.09.17 - SLIDE 26IS 202 – FALL 2002
New Solutions for Creating Metadata
After Capture During Capture
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2002.09.17 - SLIDE 27IS 202 – FALL 2002
After Capture: Media Streams
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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
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2002.09.17 - SLIDE 29IS 202 – FALL 2002
Media Streams GUI Components
• Media Time Line
• Icon Space– Icon Workshop– Icon Palette
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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
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2002.09.17 - SLIDE 31IS 202 – FALL 2002
Media Time Line
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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
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2002.09.17 - SLIDE 33IS 202 – FALL 2002
Icon Space
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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
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2002.09.17 - SLIDE 35IS 202 – FALL 2002
Icon Space: Icon Workshop Detail
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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
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2002.09.17 - SLIDE 37IS 202 – FALL 2002
Icon Space: Icon Palette Detail
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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
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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
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2002.09.17 - SLIDE 40IS 202 – FALL 2002
New Solutions for Creating Metadata
After Capture During Capture
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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
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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
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2002.09.17 - SLIDE 43IS 202 – FALL 2002
Active Capture
Processing
Capture Interaction
ActiveCapture
ComputerVision
HCI
Direction/Cinematography
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2002.09.17 - SLIDE 44IS 202 – FALL 2002
Automated Capture: Good Capture
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2002.09.17 - SLIDE 45IS 202 – FALL 2002
Automated Capture: Error Handling
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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
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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
Parser
Producer
Media
Media
Media
ContentRepresentation
ContentRepresentation
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2002.09.17 - SLIDE 48IS 202 – FALL 2002
Jim Lanahan in an MCI Ad
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2002.09.17 - SLIDE 49IS 202 – FALL 2002
Jim Lanahan in an @Home Banner
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2002.09.17 - SLIDE 50IS 202 – FALL 2002
Automated Media Production Process
2 Annotationand Retrieval
Asset Retrieval and Reuse
Web Integration and
Streaming MediaServices
FlashGenerator
WAP
HTML Email
Print/PhysicalMedia
AutomatedCapture
1Automatic
Editing3
PersonalizedDelivery
4
Annotation ofMedia Assets
Reusable Online Asset Database
Adaptive Media Engine
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2002.09.17 - SLIDE 51IS 202 – FALL 2002
Proposed Technology Architecture
Media Processing
DB
Analysis Engine
Interaction Engine
Adaptive MediaEngine
Annotation andRetrieval Engine
(MPEG 7)
DeliveryEngine
OSMedia
CaptureFile
AVOut
NetworkDeviceControl
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2002.09.17 - SLIDE 52IS 202 – FALL 2002
Lecture 08: Media Streams
• Problem Setting
• Representing Media
• Current Approaches
• New Solutions
• Methodological Considerations
• Future Work
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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
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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
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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
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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)
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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)