CALL FOR PAPERS
ICML Workshop
Machine Learning in Computer Vision
July 9, 2002, Sydney, Australia
http://www.cse.unsw.edu.au/~icml2002/workshops/MLCV02ws.html
WORKSHOP DESCRIPTION
Learning is one of the current frontiers for computer vision research and has
been receiving increased attention in recent years. Machine learning technology
has strong potential to contribute to: - the development of flexible and robust
vision algorithms that will improve the performance of practical vision systems
with a higher level of competence and greater generality, and - the development
of architectures that will speed up system development time and provide better
performance.
The goal of improving the performance of computer vision systems has brought new
challenges to the field of machine learning, for example, learning from
structured descriptions, partial information, incremental learning, focusing
attention or learning regions of interests (ROI), learning with many classes.
Solving problems in visual domains will result in the development of new, more
robust machine learning algorithms that will be able to work in more realistic
settings.
>From the standpoint of computer vision systems, machine learning can offer
effective methods for automating the acquisition of visual models, adapting task
parameters and representation, transforming signals to symbols, building
trainable image processing systems, focusing attention on target object and
learning when to apply what algorithm in a vision system.
>From the standpoint of machine learning systems, computer vision can provide
interesting and challenging problems for example: learning models rather than
hand- crafting them, learning to transfer experience gained in one application
domain to another domain, learning from large sets of images with no annotation,
designing evaluation criteria for the quality of learning processes in computer
vision systems.
TOPICS
As learning in vision is a new area of research, there are many unexplored issues
and potentially many different ways in which learning can be applied to solve
vision problems. This workshop seeks to provide a broad forum for the
presentation and discussion of new ideas and contemporary issues. Therefore two
kinds of papers are invited:
- "position papers" based on theory or experience
- deep technical papers of interest to specialists in the area.
Topics of interest include, but are not limited to:
* Learning to recognize shapes
* Supervised learning of visual models
* Unsupervised learning for structure detection in images
* Multistrategy and meta learning in vision
* Learning and refining visual models
* Multi-level learning and reuse of learned concepts
* Learning important features for image analysis
* Relational learning in vision
* Context in visual learning
* Mining from large collections of images and videos
* Interpretation of discovered visual models
* Image segmentation via learning
* Probabilistic model estimation and selection
* Applications such as medical imaging, object recognition, remote sensing,
digital maps, document image analysis and recognition, spatial reasoning
WORKSHOP FORMAT
The workshop is aimed to be a high communicative meeting place for researchers
working on similar topics, but from different communities. In order to achieve
these goals, the workshop will consist of short presentations and longer
discussions. Each author will be encouraged to read another accepted paper and to
comment on it after the original talk was given. A panel session at the end of
the workshop will summarise what has been learned from the workshop and will
identify future directions.
Authors are invited to submit original research contributions or experience
reports. Submitted papers will be reviewed by referees from the Program
Committee. Accepted papers will be published in the working notes provided by
ICML-2002.
IMPORTANT DATES
Submission deadline: 22 April 2002
Notification to participants: 10 May 2002
Camera-ready copies: 31 May 2002
Workshop date: 9 July 2002
SUBMISSION INFORMATION
Papers should be double-spaced and no longer than 12 pages. The preferred method
of submission is by electronic mail, in PostScript or PDF form. The format of the
submissions should follow the ICML-2002 recommendations available at
http://www.cse.unsw.edu.au/~icml2002/format.html. For those using LaTex, a style
file is available. Submissions should be sent to tatjana@cse.unsw.edu.au by
April 22, 2002. Subject: MLCV-2002 workshop submission paper.
ORGANISERS
PROGRAM CO-CHAIRS
Arcot Sowmya
University of New South Wales
NSW 2052, AUSTRALIA
E-mail sowmya@cse.unsw.edu.au
Tatjana Zrimec
University of New South Wales
NSW 2052, AUSTRALIA
E-mail tatjana@cse.unsw.edu.au
PROGRAM COMMITTEE
Joachim M. Buhmann, University of Bonn, Germany
Terry Caelli, The University of Alberta, Alberta, Canada
Igor Kononenko, University of Ljubljana, Slovenia
Donato Malerba, University of Bari, Italy
Ram Nevatia, University of Southern California, USA
Svetha Venkatesh, Curtin University, Australia
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