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Title:

Digital Video Library Network

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News, TV, Movie...etc. Search and Discovery. Automated extraction of ... Consumer and business access to news and information of interest. Entertainment ... – PowerPoint PPT presentation

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Title: Digital Video Library Network


1
Digital Video Library Network
  • Supervisor Prof. Michael Lyu
  • Student Ma Chak Kei, Jacky

2
Introduction
  • Overview
  • System Architecture
  • Video Server
  • Indexing Server
  • Query Server
  • Client Applications
  • Related Technology

3
Overview
  • Make large video library to be searchable
    information resources
  • Video
  • Captures the experience of society
  • News, TV, Movieetc
  • Search and Discovery
  • Automated extraction of knowledge from video
  • Integration of speech, image, and natural
    language understanding for library creation and
    exploration

4
Information Retrieval
  • Given a large collection of multimedia records,
    find similar/interesting things
  • Allow fast, approximate queries
  • Find rules/patterns
  • Similarity search
  • Find pairs of documents that are similar
  • Find medical cases similar to Smiths
  • Find pairs of stocks that move in sync

5
Application Areas
  • Education and training
  • Consumer and business access to news and
    information of interest
  • Entertainment
  • Interactive television
  • Meeting/corporate memory
  • Video conferences

6
Diverse Technologies
  • Image Understanding
  • Scene Understanding
  • Speech Recognition
  • Metadata/Entity Extraction
  • Natural Language Processing
  • More
  • Database, Network, User Interface...

7
System Architecture
  • Component Based
  • High Extensibility
  • High Availability
  • High Performance
  • Workstation or Distributed Systems over Internet

8
System Architecture
9
System Architecture
10
Video Server
  • Specialized in capturing, storing, and delivery
    videos
  • Dual with different video sources
  • Features
  • Video Storage
  • Meta-Media Attributes
  • Video Delivery

11
Video Storage
  • Store segmented video in digital formats
  • Video segmentation
  • Using low-level visual features
  • Using multimedia cues
  • Semantic segmentation
  • Using audio, visual, textual signals at different
    stages
  • For Example use audio feature to separate speech
    and commercials then use text analysis to do
    story-level segmentation
  • Require knowledge on the video source

12
Meta-Media Attributes
  • For information
  • related to but not within the video
  • impossible to be extracted from the video
  • Five baisc types
  • Production feature
  • Media feature
  • Text description
  • Intellectual property information
  • References

13
Video Delivery
  • Main concern
  • number of current clients
  • quality of services
  • Streaming protocol
  • reduce the latency for starting the video
  • exploit the error tolerance nature of video
  • QoS
  • User perspective
  • Application perspective
  • Transmission perspective

14
QoS Perspectives
15
QoS Processing Model
16
Indexing Server
  • Specialized in indexing the video for retrieval
    use
  • Features to be indexed
  • Textual Information
  • Physical Features
  • Semantic Features
  • Advanced indexing on
  • Video caption
  • Company logo
  • Face recognition

17
Textual Information
  • Includes
  • Provided meta-media attributes
  • Generated script by automatic speech recognition
  • Tradition information retrieval for text
    documents
  • Lexical analysis
  • Removal of stopwords
  • Stemming
  • Selection of index terms
  • Construction of term categorization structures

18
Speech Recognition
19
Physical Features
  • Low-level objects and associated features
  • Features indexed
  • Color
  • Texture
  • Shape
  • Motion
  • Spatiotemporal structures

20
Extract Physical Features
  • Segment the video into separate shots
  • Consistent background scene
  • Extract salient video regions and video objects
  • Index video objects with features mentioned
  • Advanced video object extraction in MPEG-4

21
Semantic Features
  • More intuitive and direct then physical features
  • Probabilistic graphic model
  • By Hidden Markov Model (HMM) to investigate the
    combination of input features that represent an
    object
  • Identify events, objects, and sites
  • Using multimedia training data
  • Limit the lifetime of objects to the shots
    duration
  • Compute probabilities ofP(car AND road segment
    of multimedia data)
  • Higher level HMM between different
    objects(Markov chain Monte Carlo method)

22
Complexity of Features
23
Query Server
  • Transform user query to formal queries
  • Natural language processing
  • Ranking of results
  • Different IR Models
  • Boolean Model
  • Vector Model
  • Probabilistic Model
  • Have knowledge of individual Indexing Servers
  • Multimedia Portals!

24
Client Applications
  • Basic functionality
  • Query
  • Presentation of Results
  • Video Playback
  • Additional functionality
  • Linkage to external database
  • Manipulation of video

25
MPEG4
  • Standard to address multimedia contents
  • Represent units of aural, visual or audiovisual
    content as media objects
  • Natural or synthetic origin
  • Compose the scene by description of media objects
  • Support QoS in a media-object level
  • Indexing of media-object become easy

26
MPEG7
  • Standard to describe the multimedia content data
    with some degree of interpretation of the
    semantics
  • Act as the interface for multimedia applications
  • e.g. Between Video Server and Indexing Server

27
Conclusion
  • Challenges
  • Multilingual Processing
  • Cognitive Processing
  • Library Interoperability
  • Intellectual Property
  • Security Issues

28
Thank you
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