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 http://visi.edmgr.com , 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, mailto:Bill.Triggs@imag.fr - Chris Williams, mailto:ckiw@inf.ed.ac.uk