2003.09.23 - SLIDE 1IS 202 – FALL 2003

Lecture 10: Metadata for Media

Prof. Ray Larson & Prof. Marc Davis

UC Berkeley SIMS

Tuesday and Thursday 10:30 am - 12:00 pm

Fall 2003http://www.sims.berkeley.edu/academics/courses/is202/f03/

SIMS 202:

Information Organization

and Retrieval

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Today’s Agenda

• Review of Last Time

• Metadata for Motion Pictures– Representing Video

– Current Approaches

– Media Streams

• Discussion Questions

• Action Items for Next Time

2003.09.23 - SLIDE 3IS 202 – FALL 2003

Today’s Agenda

• Review of Last Time

• Metadata for Motion Pictures– Representing Video

– Current Approaches

– Media Streams

• Discussion Questions

• Action Items for Next Time

2003.09.23 - SLIDE 4IS 202 – FALL 2003

The Media Opportunity

• Vastly more media will be produced• Without ways to manage it (metadata

creation and use) we lose the advantages of digital media

• Most current approaches are insufficient and perhaps misguided

• Great opportunity for innovation and invention

• Need interdisciplinary approaches to the problem

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What is the Problem?

• Today people cannot easily 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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M E T A D A T AMETADATA

Traditional Media Production Chain

PRE-PRODUCTION POST-PRODUCTIONPRODUCTION DISTRIBUTION

Metadata-Centric Production Chain

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Asset Retrieval and Reuse

Automated Media Production Process

Web Integration and

Streaming MediaServices

FlashGenerator

WAP

HTML Email

Print/PhysicalMedia

ActiveCapture

1Automatic

Editing3

Personalized/Customized

Delivery

4

Adaptive Media Engine

2 Annotationand Retrieval

Reusable Online Asset Database

Annotation ofMedia Assets

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Technology Summary

• Media Streams provides a framework for creating metadata throughout the media production cycle to make media assets searchable and reusable

• Active Capture automates direction and cinematography using real-time audio-video analysis in an interactive control loop to create reusable media assets

• Adaptive Media uses adaptive media templates and automatic editing functions to mass customize and personalize media and thereby eliminate the need for editing on the part of end users

• Together, these technologies will automate, personalize, and speed up media production, distribution, and reuse

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Active Capture

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Active Capture: Reusable Shots

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Marc Davis in Godzilla Scene

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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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• 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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Today’s Agenda

• Review of Last Time

• Metadata for Motion Pictures– Representing Video

– Current Approaches

– Media Streams

• Discussion Questions

• Action Items for Next Time

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Representing Video

• Streams vs. Clips

• Video syntax and semantics

• Ontological issues in video representation

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Video is Temporal

Stream of 100 Frames of Video

A Clip from Frame 47 to Frame 68 with Descriptors

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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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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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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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Ontological Issues for Video

• Video plays with rules for identity and continuity

– Space

– Time

– Person

– Action

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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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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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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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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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“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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Today’s Agenda

• Review of Last Time

• Metadata for Motion Pictures– Representing Video

– Current Approaches

– Media Streams

• Discussion Questions

• Action Items for Next Time

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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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Signal-Based Parsing

• Practical problem– Parsing unstructured, unknown video is very,

very hard

• Theoretical problem– Mismatch between percepts and concepts

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Perceptual/Conceptual Issue

Clown Nose Red Sun

Similar Percepts / Dissimilar Concepts

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Perceptual/Conceptual Issue

Car Car

Dissimilar Percepts / Similar Concepts

John Dillinger’s Timothy McVeigh’s

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Signal-Based Parsing

• Effective and useful automatic parsing

– Video• Shot boundary detection• Camera motion analysis• Low level visual similarity• Feature tracking• Face detection

– Audio• Pause detection• Audio pattern matching• Simple speech recognition• Speech vs. music

detection

• Approaches to automated parsing

– At the point of capture, integrate the recording device, the environment, and agents in the environment into an interactive system

– After capture, use “human-in-the-loop” algorithms to leverage human and machine intelligence

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Keywords vs. Semantic Descriptors

dog,biting,Steve

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Keywords vs. Semantic Descriptors

dog,biting,Steve

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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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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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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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Notation for Time-Based Media: Music

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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

2003.09.23 - SLIDE 39IS 202 – FALL 2003

Today’s Agenda

• Review of Last Time

• Metadata for Motion Pictures– Representing Video

– Current Approaches

– Media Streams

• Discussion Questions

• Action Items for Next Time

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After Capture: Media Streams

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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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Media Streams GUI Components

• Media Time Line

• Icon Space– Icon Workshop– Icon Palette

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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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Media Time Line

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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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Icon Space

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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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Icon Space: Icon Workshop Detail

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Icon Space: Icon Palette

• Dynamically group related sets of iconic descriptors

• Collect icon sentences

• Reuse descriptive effort of others

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Icon Space: Icon Palette Detail

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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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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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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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Today’s Agenda

• Review of Last Time

• Metadata for Motion Pictures– Representing Video

– Current Approaches

– Media Streams

• Discussion Questions

• Action Items for Next Time

2003.09.23 - SLIDE 55IS 202 – FALL 2003

Discussion Questions (Davis)

• John Snydal on Media Streams– What is the target audience of users (annotators/retrievers) for

Media Streams? In the article the following groups are mentioned:

• Content providers• Video editors• News teams• Documentary film makers• Film archives• Stock photo houses• Video archivists• Video producers• (international audience)• (illiterate and preliterate people)

– Is it possible that Media Streams could satisfy the needs, goals and requirements of all of these groups, or would it be more appropriate to develop separate, tailored applications for the unique needs of each group?

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Discussion Questions (Davis)

• danah boyd on Media Streams– Icons require visual literacy. Icons are also

culturally constructed. Thus, for them to work as an information access bit, people must learn the visual language; it is not inherent. What are the social consequences of a system dependent on unfamiliar cues?

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Discussion Questions (Davis)

• danah boyd on Media Streams– Films are constructed narratives. But most

commonplace storytelling is not. Even in a creative form, people often piece together found objects instead of finding objects to fit their story. (Think teenage girls making collages out of the latest YM.) Storytelling also happens around media far more than through media (i.e. telling a story about a picture rather than using a collection of pictures to tell a story). My guess is that this social phenomenon goes beyond the retrieval issues. Do you think that Media Streams would encourage new behavior regarding storytelling or will it only be useful for those with a constructed narrative in mind? Why (not)?

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Discussion Questions (Davis)

• Jesse Mendelsohn on Media Streams– Media Streams does not allow iconic

descriptions of emotion or scene-interpretation. How would someone searching stock footage for a “suspenseful scene of two men beating each other” go about doing it? The actual sense of “suspense” and the act of “beating” cannot be iconified. Does this limit Media Streams' ability or is there a way around it within its capabilities as described?

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Discussion Questions (Davis)

• Jesse Mendelsohn on Media Streams– In order for Media Streams to work well it

relies on a the availability of a very large and extensive resource of well-annotated video. Is the current annotation process too primitive and/or time consuming to allow Media Streams to work to its full potential? Will changing how Media Streams can be used to annotate video or changing video annotation methods in general make Media Streams more effective?

2003.09.23 - SLIDE 60IS 202 – FALL 2003

Today’s Agenda

• Review of Last Time

• Metadata for Motion Pictures– Representing Video

– Current Approaches

– Media Streams

• Discussion Questions

• Action Items for Next Time

2003.09.23 - SLIDE 61IS 202 – FALL 2003

Assignment 4.1

• Assignment 4.1

• Phone Metadata Design - Part 1– Due Oct 2

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Next Time

• Database Design (RRL)

• Readings– Handouts in Class

• Database Modeling and Design -- Ch. 2 The ER Model - Basic Concepts (Teorey, T.J.)

• Logical Database Design and the Relational Model (F. R. McFadden, J. A. Hoffer)