CALL FOR PAPERS

   Special Issue of the International Journal of Computer Vision on

          PROBABILISTIC MODELS FOR IMAGE UNDERSTANDING

Aims and scope: Probabilistic models provide a compelling framework
for describing image and video content at levels ranging from small
image patches to overall scene and motion structure.  We solicit
papers describing the development, learning and use of principled
probabilistic models for image understanding.  Relevant topics include
(but are not limited to):

- low level models (image patches, random fields),
- object recognition / detection,
- structural models / image parsing,
- structured models of human motion,
- probabilistic frameworks for image representation,
- efficient algorithms for learning such models,
- frameworks and datasets for evaluating such models.

We are particularly interested in models:

- that incorporate rich structure (deep, graphical, hierarchical,
   compositional,...) or that are suited for use within structure-based
   frameworks,

- or that minimize the amount of labelled data required for learning
   new classes, by exploiting latent structure or reusing components
   or priors.

We will consider submissions describing specific models, position
papers and evaluation papers:

- Papers on specific models should either give enough detail for a
   moderately skilled graduate student to reimplement the exact model
   used and reproduce the experimental results, or provide a full
   description or an open source implementation as supplementary
   material. They must also include a discussion of competing
   approaches and comparative testing that establishes the advantages
   and limitations of the approach presented.

- Position papers should clearly present the arguments for and against
   one or more generic approaches, supporting these with indicative
   experimental results, comparative tables and thorough discussions of
   the existing literature.

- Evaluation papers should describe a benchmark or challenge problem,
   motivating it by discussing limitations of existing models or
   benchmarks or debates regarding performance that need to be
   resolved, presenting the detailed evaluation methodology and the
   dataset (coverage, collection, labelling), and presenting a
   representative sample of benchmark results for baseline methods or
   recent methods from the literature. The benchmark and dataset must
   be non-proprietary and publicly available so that other authors can
   test their own methods on it. If possible open source
   implementations of the baseline models and feature sets should also
   be made available.

Submissions: Papers following the usual IJCV author guidelines should
be submitted to the IJCV website , choosing the
Special Issue article type "Probabilistic Models for Image
Understanding".  Regular journal articles (25 pages) are preferred,
but short papers (10 pages) and well-balanced surveys (30 pages) will
also be considered.  All submissions will be subject to peer review.

Submissions will be returned without review if we feel that they are
not well aligned with the goals of the special issue.  If you are
unsure whether an intended submission is in scope, send an abstract or
a draft to the editors of the special issue at least one month before
the submission deadline.

Submission deadline: July 21 2008
Scheduled publication date: Summer 2009

Guest editors:
- Bill Triggs, Bill.Triggs@imag.fr
- Chris Williams, ckiw@inf.ed.ac.uk