DIMACS Workshop on Video Mining

November 4-6, 2002
DIMACS Center, Rutgers University, Piscataway, New Jersey

Organizers: 
    Azriel Rosenfeld, University of Maryland, ar@cfar.umd.edu 
    Daniel DeMenthon, University of Maryland, daniel@cfar.umd.edu 
    Dave Doermann, University of Maryland, doermann@cfar.umd.edu 

Presented under the auspices of the Special Focus on Data Analysis and
Mining.  


Modern computer technology, together with the proliferation of
broadcast channels and of video-based surveillance systems, has
enabled us to produce vast amounts of both raw and processed video
data. The potential uses of this data are many and varied. Monitoring
and mining of the content of this already huge, rapidly growing mass
of data calls for the development of major computational resources and
the development of sophisticated video understanding techniques.

The applications and potential applications of video mining include:

- monitoring of (possibly remote) surveillance cameras in theft
protection, fire protection, care of bedridden patients and young
children

- automatic recognition of suspicious people in large crowds

- automatic checking to identify passengers entering an airplane, bus,
or public building (to verify entry authorization)

- automatic quality control in manufacturing processes

- robotic vision

- retrieval of archived video clips to illustrate a newsbreaking story

- retrieval of suspicious activities in prerecorded video surveillance
sequences

- browsing of DVD and set-top box recordings

- intelligent fast-forward techniques

- detection of multi-lingual scene text to determine origin of
broadcast

- video search engines for web browsers

- ranking of video clips by relevance in web query results

- classification of videos into genres for search pruning

- detection of crowd patterns for mob control

- detection of traffic patterns for traffic understanding management

There are some pervasive challenges here. A huge amount of data must
be efficiently stored. Once stored, there must exist efficient
retrieval algorithms. It must also be possible to retrieve different
kinds of data stored in possibly different formats. Some of the
applications will require real-time response and action based on an
incoming data stream.

The purpose of this workshop will be to survey available and potential
technologies for video monitoring and mining (and in general methods
of fast and efficient content-based analysis of video streams) and to
identify promising directions for research in this challenging
area. Specific topics to be covered will be analysis of camera motion
and scene activity; temporal segmentation; content-based
classification, indexing, and retrieval; representation, browsing, and
visualization.

We will also investigate related mining problems having to do with
audio mining, seismic data mining, and cross-modality mining. Audio
mining applications of interest include speech recognition; seismic
data mining applications include identification of potential new
sources of oil and gas and detection of earthquakes and/or nuclear
tests. Cross-modality issues arise for example in problems involving
identification from both video and speech.


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Workshop Program: (FINAL)

Monday, November 4th


8:00 - 9:00       Registration and breakfast

9:00 - 9:05       Welcome and Greeting
                  DIMACS representative

9:05 - 9:20       Introductory remarks
                  Azriel Rosenfeld, University of Maryland, College Park

9:20 - 10:10      Spatiotemporal Representations from Image Sequences: From 
                  Illusions to Video Mining
                  Yiannis Aloimonos, University of Maryland, College Park

10:10 - 10:30     Break

10:30 - 11:15     Video Indexing and Summarization using the Motion Activity 
                  Descriptor
                  Ajay Divakaran, Mitsubishi Electric Research Laboratories

11:15 - 12:00     A Framework of Human Motion Tracking and Event Detection for
                  Video Indexing and Mining
                  Thomas Huang, University of Illinois at Urbana-Champaign

12:00 - 1:30      Lunch

1:30 - 2:15       Efficient Video Browsing using Multiple Synchronized Views
                  Arnon Amir, IBM Almaden Research Center

2:15 - 3:00       Video Indexing, Summarization, and Adaptation
                  Shih-Fu Chang, Columbia University

3:00 - 3:15       Break

3:15 - 4:00       Beyond Key Frames: The Physical Setting as a Video Mining
                  Primitive
                  John R. Kender, Columbia University

4:00 - 4:45       Content-based Video Retrieval
                  Rainer Lienhart, Intel Laboratories


Tuesday, November 5th


8:00 - 9:00       Registration and breakfast

9:00 - 9:45       Video Indexing and Retrieval using Spatio-Temporal 
                  Descriptions of Sequences
                  Daniel DeMenthon, University of Maryland, College Park

9:45 - 10:30      Database Techniques and Video Data Management
                  Arjen P. de Vries, CWI, The Netherlands
   
10:30 - 10:45     Break

10:45 - 11:30     Automatic Genre Classification of Video
                  David Doermann, University of Maryland, College Park

11:30 - 12:15     Mining Images and Video
                  B.S. Manjunath, University of California, Santa Barbara

12:15 - 1:30      Lunch

1:30 - 2:15       Knowledge-based Techniques for Content-based Video Retrieval
                  Milan Petkovic, University of Twente

2:15 - 3:00       Video Categorization using Semantics and Semiotics
                  Mubarak Shah, University of Central Florida

3:00 - 3:15       Break

3:15 - 4:00       Mixtures of Probability Experts for Audio Retrieval and
                  Indexing
                  Malcolm Slaney, IBM Almaden Research Center

4:00 - 4:40       Statistical Modeling and Retrieval of Video Content
                  John R. Smith, IBM T.J. Watson Research Center


Wednesday, November 6th


8:00 - 9:00       Registration and breakfast

9:00 - 9:45       Multimedia Story Segmentation
                  Nevenka Dimitrova, Philips Research USA
        
9:45 - 10:30      Context-dependent Detection of Unpredictable Events in
                  Videos
                  Longin Jan Latecki, Temple University

10:30 - 10:45     Break

10:45 - 11:30     Statistical Methods for Real-time Video Surveillance
                  Visvanathan Ramesh, Siemens Corporate Research

11:30 - 12:15     Bayesian Models of Video Structure for Segmentation and
                  Content Characterization
                  Nuno Vasconcelos, Compaq Research

12:15 - 1:30      Lunch
1:30 - 2:15       Movie Content Analysis and Abstraction via Multimodal
                  Information
                  C.-C. Jay Kuo, University of Southern California

2:15 - 3:00       Finding Information in a Digital Video Archive
                  Alexander Hauptmann, Carnegie-Mellon University

3:00 - 3:15       Concluding Remarks


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REGISTRATION FEES:

Our funding agencies require that we charge a registration fee during
the course of the workshop. Registration fees include participation in
the workshop, all workshop materials, breakfast, lunch, breaks and any
scheduled social events (if applicable). Fees are $40 per person per
day for faculty, researchers and "other", and $5 per person per day
for postdocs.

The registration fee is waived for undergraduate students, graduate
students, DIMACS postdocs and DIMACS long-term visitors who are in
residence at DIMACS. Fees for employees of DIMACS partner institutions
are waived. DIMACS partner institutions are: Rutgers University,
Princeton University, AT&T Labs - Research, Bell Labs, NEC Research
Institute and Telcordia Technologies. Fees for employees of DIMACS
affiliate members Avaya Labs and Microsoft Research are also
waived. Fees are not waived for IBM Watson Research Center employees
(the terms of the IBM membership are different from the Avaya and
Microsoft agreements).


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For complete information on registration, travel and accommodations see: 


http://dimacs.rutgers.edu/Workshops/Video/