CS 430: Information Discovery
Lecture 22
Non-Textual Materials 2
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Course Administration
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Surrogates for Non-textual Materials
Discovery of non-textual materials requires surrogates
• How far can these surrogates be created automatically?
• Automatically created surrogates are much less expensive than
manually created, but have high error rates.
• If surrogates have high rates of error, is it possible to have
effective information discovery?
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Informedia Digital Video Library
Collections: Segments of video programs, e.g., TV and radio
news and documentary broadcasts. Cable Network News,
British Open University, WQED television.
Segmentation: Automatically broken into short segments of
video, such as the individual items in a news broadcast.
Size: More than 2,000 hours, 1 terabyte.
Objective: Research into automatic methods for organizing and
retrieving information from video.
Funding: NSF, DARPA, NASA and others.
Principal investigator: Howard Wactlar (Carnegie Mellon
University).
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Informedia Digital Video Library
History
• Carnegie Mellon has broad research programs in speech
recognition, image recognition, natural language processing.
• 1994. Basic mock-up demonstrated the general concept of a
system using speech recognition to build an index from a sound
track matched against spoken queries. (DARPA funded.)
• 1994-1998. Informedia developed the concept of multi-modal
information discovery with a series of users interface
experiments. (NSF/DARPA/NASA Digital Libraries Initiative.)
• 1998 - . Continued research and commercial spin-off.
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The Challenge
A video sequence is awkward for information discovery:
• Textual methods of information retrieval cannot be applied
• Browsing requires the user to view the sequence. Fast skimming
is difficult.
• Computing requirements are demanding (MPEG-1 requires 1.2
Mbits/sec).
Surrogates are required
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Multi-Modal Information Discovery
The multi-modal approach to information retrieval
Computer programs to analyze video materials for clues
e.g., changes of scene
• methods from artificial intelligence, e.g., speech
recognition, natural language processing, image
recognition.
• analysis of video track, sound track, closed captioning if
present, any other information.
Each mode gives imperfect information. Therefore use
many approaches and combine the evidence.
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Informedia Library Creation
Video
Audio
Text
Speech recognition
Image extraction
Natural language
interpretation
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Segmentation
Segments
with derived
metadata
Informedia: Information Discovery
User
Querying via
natural
language
Requested segments
and metadata
Segments
with derived
metadata
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Browsing via
multimedia
surrogates
Text Extraction
Source
Sound track: Automatic speech recognition using Sphinx II and III
recognition systems. (Unrestricted vocabulary, speaker independent,
multi-lingual, background sounds). Error rates 25% up.
Closed captions: Digitally encoded text. (Not on all video. Often
inaccurate.)
Text on screen: Can be extracted by image recognition and optical
character recognition. (Matches speaker with name.)
Query
Spoken query: Automatic speech recognition using the same system
as is used to index the sound track.
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Typed by user
Image Understanding
Informedia has developed specialized tools for
various aspects of image understanding
• scene break detection
segmentation
icon selection
• image similarity matching
• camera motion and object tracking
• video-OCR (recognize text on screen)
• face detection and association
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Multimodal Metadata Extraction
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An Evaluation Experiment
Test corpus:
• 602 news stories from CNN, etc. Average length 672 words.
• Manually transcribed to obtained accurate text.
• Speech recognition of text using Sphinx II (50.7% error rate)
• Errors introduced artificially to give error rates from 0% to 80%.
• Relative precision and recall (using a vector ranking) were used
as measures of retrieval performance.
As word error rate increased from 0% to 50%:
• Relative precision fell from 80% to 65%
• Relative recall fell from 90% to 80%
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Speech recognition and retrieval
performance
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User Interface Concepts
Users need a variety of ways to search and browse, depending
on the task being carried out and preferred style of working
• Visual icons
one-line headlines
film strip views
video skims
transcript following of audio track
• Collages
• Semantic zooming
• Results set
• Named faces
• Skimming
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Thumbnails, Filmstrips and Video
Skims
Thumbnail:
• A single image that illustrates the content of a video
Filmstrip:
• A sequence of thumbnails that illustrate the flow of a video
segment
Video skim:
• A short video that summarizes the contents of a longer sequence,
by combining shorter sequences of video and sound that provide
an overview of the full sequence
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Creating a Filmstrip
Separate video sequence into shots
• Use techniques from image recognition to identify dramatic
changes in scene. Frames with similar color characteristics are
assumed to be part of a single shot.
Choose a sample frame
• Default is to select the middle frame from the shot.
• If camera motion, select frame where motion ends.
User feedback:
• Frames are tied to time sequence.
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Creating Video Skims
Static:
• Precomputed based on video and audio phrases
• Fixed compression, e.g., one minute skim of 10 minute sequence
Dynamic:
• After a query, skim is created to emphasize context of the hit
• Variable compression selected by user
• Adjustable during playback
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Limits to Scalability
Informedia has demonstrated effective information discovery
with moderately large collections
Problems with increased scale:
• Technical -- storage, bandwidth, etc.
• Diversity of content -- difficult to tune heuristics
• User interfaces -- complexity of browsing grows with scale
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