Image and Video Retrieval and Vi

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					       Image and Video Retrieval and
                      Visual Analytics:
        Opportunities for Collaboration
                         Mike Christel,
Entertainment Technology Center, Carnegie Mellon University
Talk Outline
         (Heavy on demonstrations, light on slides….)
•   Introduction to two test corpora on my demo laptop
•   Very quick overview of CMU Informedia research
     • Speech recognition; text alignment
     • Image processing; visual concept classification
     • Language processing; named entity detection
•   Lessons Learned
•   Opportunities
CMU Informedia Video Research
• Details at:
• Speech recognition and alignment; image processing;
  named entity tagging
• Synchronized metadata for search and navigation
• Fast, direct video access to oral histories, news, etc.
• Demonstration oral history corpus: 913 hours of
  interviews from 400 individuals, 18,254 interview story
  segments (average story segment length of 3 minutes)
• Demonstration news corpus: NIST TRECVID 2006 test
  set (165 hours of U.S., Arabic, and Chinese news with
  79,484 reference shots)
  The HistoryMakers Oral History

• The world’s largest African American oral history archive
  with accomplished African Americans
• Purpose:
   • To educate and show the breadth and depth of this
     important American history as told by the first person
   • To highlight the accomplishments of individual African
     Americans across a variety of disciplines
   • To preserve this material for generations to come
• Committed to exposing the archive to the widest audience
  possible, making use of new technologies as appropriate
  The HistoryMakers Intellectual Property

• The set of 400 interviewees I will show today is in a
  corpus with planned growth to 5000
• The work is in beta test, with strict limitations on copying
  and distribution:
  “All content is the property of The HistoryMakers™:
  all proposed uses must be submitted in a proposal in
  advance to The HistoryMakers for approval before
  anything can be used and approval is totally at our
  discretion.” – Julieanna Richardson, Founder &
  Executive Director, The HistoryMakers, Chicago, IL
  A Theme for Today: User Involvement

• User Correction: Corrective action for metadata errors
  (analogous to Harry Shum’s vision at Microsoft for
  human-assisted computer vision success)
• User Control: Driving the interface to overcome
  metadata errors
• User Context: More useful interfaces driven implicitly by
  Speech Recognition Functions

• Generates transcript (if one is not given) to enable text-
  based retrieval from spoken language documents
• Improves text synchronization to audio/video in presence
  of scripts (align speech with text)
• Supplies necessary information for library segmentation
  and multimedia abstractions (e.g., break stories apart at
  silence points rather than in the middle of sentences)
    Image Understanding Functions

• Scene segmentation
• Similarity matching
• Camera motion determination and object tracking
• Optical Character Recognition (OCR) on video text and
• Face detection and recognition
• Ongoing research work in object identification and scene
  characterization, e.g., indoor/outdoor, road, building, etc.
Images Containing Similar Colors…

                       Image search with
                       tropical rainforest
                       image leads to…
Images Containing Similar Colors
Images Containing Similar Shapes
Images Containing Similar Content
           Goal: Automatic Video

Camera    Static         Static           Zoom

Objects   Adult Female   Animal           Two adults

Action    Head Motion    Left Motion      None

Captions CNN LIVE        CNN              An Online First

Scenery   Studio         Outdoor          Indoor
           Goal: Automatic Video

Camera    Static         Static           Zoom

Objects   Adult Female   Animal           Two adults

Action    Head Motion    Left Motion      None

Captions CNN LIVE        CNN              An Online First

Scenery   Studio         Outdoor          Indoor
  Automated Video Processing

• Produces descriptive metadata for video libraries
• Metadata has errors greater than metadata produced by a
  careful, human-provided annotation
• Errors in metadata can be reduced – examples to follow…
Camera and Motion Detection


Right object
motion (not
   pan left)
Text and Face Detection
Video OCR Block Diagram
             Text Area

             Text Area

               OCR         ASCII Text
Video Frames            Filtered Frames   AND-ed Frames
 (1/2 sec. intervals)
VOCR Preprocessing Problems
Augmenting VOCR with Dictionary Look-up
 Named Entity Extraction
F. Kubala, R. Schwartz, R. Stone, and R. Weischedel, “Named Entity Extraction from Speech”, Proc.
   DARPA Workshop on Broadcast News Understanding Systems, Lansdowne, VA, February 1998.

     CNN national correspondent John Holliman is at Hartsfield
   International Airport in Atlanta. Good morning, John. …But
    there was one situation here at Hartsfield where one airplane
     flying from Atlanta to Newark, New Jersey yesterday had a
        mechanical problem and it caused a backup that spread
   throughout the whole system because even though there were
     a lot of planes flying to the New York area from the Atlanta
                           area yesterday, ….

                  Key: Place, Time, Organization/Person
Improving the Interface via Usage Context
     Example: query-based thumbnail selection
Improving Utility through End-User Control
    Example: filtering storyboard based on visual
   concepts with user controlling precision and recall
Improving the Metadata via User Interaction
  • Example: collecting positive and implicit negative sets of
    labeled shot data for visual concepts
  • Reference: Ming-yu Chen, et al., ACM Multimedia 2005
  Automated Video Processing
• Produces descriptive metadata for video libraries
• Metadata has errors greater than metadata produced by a
  careful, human-provided annotation
• Errors in metadata can be reduced:
   • By more computation-intensive algorithms
   • By taking advantage of video frame-to-frame redundancy
   • By folding in context, e.g., probable text sizes in video
   • By folding in extra sources of knowledge, e.g., a dictionary
     for cleaning up VOCR, or labeled data revealing patterns
     for named entity detection
   • By human review and correction, which can generate
     additional labeled data for machine learning
Storyboards: TRECVID Search Success
 • For the shot-based directed search information retrieval
   task evaluated at TRECVID, storyboards have
   consistently and overwhelmingly produced top scores
 • Motivated users can navigate through thousands of
   shot thumbnails in storyboards, better even than with
   “extreme video retrieval” interfaces: 2487 shots on
   average per 15 minute topic for TRECVID 2006
   (Christel & Yan, CIVR 2007)
 • Storyboard benefits: packed visual overview, trivial
   interactive control needed for “overview, zoom and
   filter, details on demand” – Shneiderman’s Visual
   Information-Seeking Mantra
  Beyond Fact-Finding
• CACM (April 2006), Info. Processing and Mgt (March
  2008), etc., have special issues on this topic
• G. Marchionini (“Exploratory Search: From Finding to
  Understanding,” CACM 49, April 2006) breaks down 3
  types of search activities:
    • Lookup (fact-finding; solving stated/understood need)
    • Learn
    • Investigate
• Computer scientists and information retrieval specialists
  emphasize evaluation of lookup activities (NIST TREC)
• Real world interest in learn/investigate: for an oral history
  collection, State Univ. New York at Buffalo Workshop
  library science and humanities participants quite
  interested in learn/investigate activities
Exploratory Search
• Examples where storyboards still useful: visual review
• Where storyboards fail:
   • Showing other facets like time, space, co-occurrence,
     named entities (When did disasters occur? Where?)
   • Providing collection understanding, holistic view of
     what’s in 100s of segments of 1000s of shots
   • Providing window into visually homogenous results,
     e.g., results from color search perhaps, or a corpus of
     just lectures, or head-and-shoulder interview shots
• Claim: Storyboards are not sufficient, but are part of a
  useful suite of tools/interfaces for interactive video search
  Anecdotal Support for Claim
• Collected 2006-2007 from:
   • Government analysts with news data
   • History students and faculty with oral history data
• Views Tested:
   • Timeline
   • Visual Info Browsing Environment (VIBE) Plot
   • Map View
   • Named Entity view (people, places, organizations)
   • Text-dominant views:
      • Nested Lists (pre-defined clusters by contributor)
      • Common Text (on-the-fly grouping of common phrases)
  Anecdotal Results
• 38 HistoryMakers corpus users (mostly students, 15
  female, average age 24), experienced web searchers,
  modest digital video experience
• 6 intelligence analysts (1 female; 2 older than 40, 3 in
  their 30s, 1 in 20s), very experienced text searchers,
  experienced web searchers, novice video searchers
• View use minimal aside from Common Text
• Text titling and text transcripts used frequently
• A bit of evidence for collection understanding (e.g., diffs in
  topic between New York and Chicago), but overall,
  cautious use of default settings for initial trial(s).
Evaluation Hurdles
• How does one evaluate information visualization for
  promoting exploratory video search?
   • Low level simple tasks vs. complex real-world tasks
   • Traditional effectiveness, efficiency, satisfaction are
     even problematic: is “fast” interface for exploration
     good or bad?
• HCI discount usability techniques offer some support, but
  ecological validity may limit impact of conclusions (e.g.,
  HCII students found Common Text well suited for History
• Look to HCI+Visual Analytics for help, e.g., Plaisant
• “First hour with system” studies, or “developer as user”
  insights too limiting. Rather, consider Multi-dimensional
  In-depth Long-term Case-studies (MILC)
Reflections –Informedia Successes
• Open benchmarking to gauge progress in digital video
  libraries and video information retrieval
    • NIST TREC Spoken Document Retrieval
• Application of machine learning techniques for visual
  classification; addressing “semantic gap” through visual
  concepts (Rong Yan, Wei-Hao Lin, Jun Yang, Robert Chen
  with Alex Hauptmann as PhD thesis advisor in LTI)
• User studies to empirically drive interface development
  (see -
  Morgan & Claypool Synthesis Lectures on Information
  Concepts, Retrieval, and Services)
Reflections – Opportunities Missed
• Foreseeing growth of the Web in 1994
• Limited use and dissemination via work with broadcasters
  and intellectual property concerns
• Significant shifts in environment, e.g., from $1,000,000 for
  a terabyte of storage in 1994 to $100 (or less) today
• Emphasis on information retrieval in traditional sense
  (lookup tasks)
Conclusions to Build Upon - 1
• “Interactive” allows human direction to compensate for
  automation shortcomings and varying needs
   • Interactive fact-finding better than automated fact-
      finding in visual shot retrieval (TRECVID)
   • Interactive computer vision has successes (Harry Shum
      at Microsoft, Michael Brown et al. at NUS)
   • Interactive view/facet control == ??? (too early to tell)
• Users need scaffolding/support to get started
• Evaluations need to run longer term, in depth, with case
  studies to see what has benefit (MILC)
Conclusions to Build Upon - 2
• Storyboards work well for visual overview
• Video surrogates can be made more effective, efficient,
  and satisfying when tailored to user activity (leverage
• Interface should provide easy tuning of precision vs. recall
• As cheap storage and transmission is producing a wealth
  of digital video, exploratory search will gain emphasis
  regarding video repositories
• Augment automatically produced metadata with human-
  provided descriptors (take advantage of what users are
  willing to volunteer, and in fact solicit additional feedback
  from humans through motivating games that allow for
  human computation, a research focus of Luis von Ahn at
  Carnegie Mellon University)

Many members of the Informedia Project, CMU research
community, and The HistoryMakers contributed to this work,
Informedia Project Director: Howard Wactlar
The HistoryMakers Executive Director: Julieanna Richardson
Informedia User Interface: Ron Conescu, Neema Moraveji
Informedia Processing: Alex Hauptmann, Ming-yu Chen, Wei-
Hao Lin, Rong Yan, Jun Yang
Informedia Library Essentials: Bob Baron, Bryan Maher
 This work supported by the National Science Foundation under
            Grant Nos. IIS-0535056 and IIS-0705491