Connectionists: CFP: IEEE PAMI Special Issue on Probabilistic Graphical Models in Computer Vision

[Erik Sudderth]

IEEE Transactions on Pattern Analysis and Machine Intelligence

       Call for Papers

Special Issue on Probabilistic Graphical Models in Computer Vision

Guest Editors: Qiang Ji, Rensselaer Polytechnic Institute; Jiebo Luo, 
Kodak Research; Dimitris Metaxas, Rutgers University; Antonio Torralba, 
Massachusetts Institute of Technology; Thomas Huang, University of 
Illinois at Urbana-Champaign, and Erik Sudderth, University of 
California at Berkeley.


Topic Description and Justification
An exciting development over the last decade has been the gradually 
widespread adoption of probabilistic graphical models (PGMs) in many 
areas of computer vision and pattern recognition.  Many problems in 
computer vision can be viewed as the search, in a specific domain, for a 
coherent global interpretation and understanding from local, uncertain, 
and ambiguous observations.  Graphical models provide a unified 
framework for representing the observations and the domain-specific 
contextual knowledge, and for performing recognition and classification 
through rigorous probabilistic inference.  In addition, PGMs readily 
capture the correlations and dependencies among the observations, as 
well as between observations and domain or commonsense knowledge, and 
allow systematic quantification and propagation of the uncertainties 
associated with data and inference.

Graphical models can be classified into directed and undirected models. 
The directed graphs include Bayesian Networks (BNs) and Hidden Markov 
Models (HMMs), while the undirected graphs include Markov Random Fields 
(MRFs) and Conditional Random Fields (CRFs).  Both directed and 
undirected graphical models have been widely used in computer vision. 
For example, HMMs are used in computer vision for motion analysis and 
activity understanding, while MRFs are extensively used for image 
labeling, segmentation, and stereo reconstruction.  The latest research 
uses BNs in computer vision for representing causal relationships such 
as for facial expression recognition, active vision, visual 
surveillance, and for data mining and pattern discovery in pattern 
recognition.  CRFs provide an appealing alternative to MRFs for 
supervised image segmentation and labeling, since they can easily 
incorporate expressive, non-local features. Another emerging trend is to 
use graphical models to integrate context and prior knowledge with 
visual cues in vision and multimedia systems.

Despite their importance and recent successes, PGMs' use in computer 
vision still has tremendous room to expand in scope, depth, and rigor. 
Their use is especially important for robust and high level visual 
understanding and interpretation.  This special issue is dedicated to 
promoting systematic and rigorous use of PGMs for various problems in 
computer vision.  We are interested in applications of PGMs in all areas 
of computer vision , including (but not limited to)

       1) image and video modeling
       2) image and video segmentation
       3) object detection
       4) object and scene recognition
       5) high level event and activity understanding
       6) motion estimation and tracking
       7) new inference and learning (both structure and parameters) 
theories for graphical models arising in vision applications
       8) generative and discriminative models
       9) models incorporating contextual, domain, or commonsense knowledge


Tentative Timelines

August 16, 2008        Submission deadline
October 25, 2008        Notification of acceptance
April 18, 2009        Camera-ready manuscript due
October 1, 2009        Targeted publication date


Paper submission and review
The papers should be submitted online through PAMI manuscript central 
site, with a note/tag designating the manuscript to this special issue. 
  All submissions will be peer-reviewed by at least 3 experts in the 
field.  Priority will be given to work with high novelty and potential 
impacts.  We will return without review submissions that we feel are not 
well aligned with our goals for the special issue.