Call for Papers – IJCV Special Issue – Structured Prediction and Inference – 26FEB2010

Dear colleagues,

we would like to announce an upcoming IJCV special issue
on STRUCTURED PREDICITON AND INFERENCE. The call for
papers follows below. A PDF version is available from
the IJCV website: http://www.springer.com/11263

Best regards,

Matthew Blaschko and Christoph Lampert

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International Journal of Computer Vision
Special Issue on
Structured Prediction and Inference
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Guest Editors
Matthew B. Blaschko, University of Oxford (blaschko@robots.ox.ac.uk)
Christoph H. Lampert, Max Planck Institute for Biological Cybernetics,
Tuebingen, Germany (chl@tuebingen.mpg.de)
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Background

Many computer visions problems can be formulated naturally as prediction
tasks of structured objects. Image segmentation, stereo reconstruction,
human pose estimation and natural scene analysis are all examples of such
problems, in which the quantity one tries to predict consists of multiple
interdependent parts. The structured output learning paradigm offers a
natural framework for such tasks, and recently introduced methods for
end-to-end discriminative training of conditional random fields (CRFs)
and structured support vector machines (S-SVMs) for image classification
and interpretation show that computer vision is not just a consumer of
existing machine learning developments in this area, but one of the driving
forces behind their development. The complexity of structured prediction
models makes the problem of inference in these models an integral part of
their analysis. While the machine learning literature has largely focused
on message passing, computer vision research has introduced novel
applications of branch-and-bound and graph cuts as inference algorithms.
Articles addressing these issues are particularly encouraged for submission
to the special issue.

Topics

Original papers are being solicited that have as topic one or more aspects
of the structured prediction framework in a computer vision setting, that
is they address the problem of prediction from an input space, such as
images
or video, to a structured and interdependent output space. Submissions can
be theoretic or applied contributions as well as position papers. Topics of
interest include, but are not limited to:

* Training for structured output learning
– Probabilistic vs. max-margin training
– Generative vs. discriminative training
– Semi-supervised or unsupervised learning
– Dealing with label noise

* Inference methods for structured output learning
– Exact vs. approximate inference techniques
– Pixel, voxel, and superpixel random field optimization
– Priors and higher order clique optimization
– Approaches that scale to large amounts of training and test data

* Computer vision applications of structured output learning
– Segmentation
– Stereo reconstruction
– Relationship between scene components
– Hierarchical models

Authors are encouraged to submit high quality, original work that has
neither appeared in, nor is under consideration by, other journals. All open
submissions will be peer reviewed subject to the standards of the journal.
Manuscripts based on previously published conference papers must be extended
substantially. Springer offers authors, editors and reviewers of the
International Journal of Computer Vision a web-enabled online manuscript
submission and review system. Our online system offers authors the ability
to track the review process of their manuscript. Manuscripts should be
submitted to: http://VISI.edmgr.com. This online system offers easy and
straightforward log-in and submission procedures, and supports a wide range
of submission file formats.

* Paper submission deadline:
– February 26, 2010
* Estimated Online Publication:
– Fall, 2010

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