Intl. Journal of Computer Vision special issue


    Learning for Vision and Vision for Learning.

CALL FOR PAPERS

Special Issue of the International Journal of Computer Vision (IJCV): 
Learning for vision and vision for learning.

Computational Vision and Machine Learning have become synergetic fields 
of research. Modern machine learning techniques have permitted large 
experimental improvements as well as a re-thinking of key problems such 
as recognition. On the other hand, vision has broadened the scope of 
machine learning offering rich and challenging new problems.

We solicit papers describing machine learning methods developed for or 
adapted to vision tasks and representations (and vice versa), such as

- priors and kernels useful for particular tasks

- machine learning algorithms addressing vision problems, e.g. fast 
detection, multi class categorization, semi supervised learning etc

- representations learned from images or videos, or optimized for visual 
inference

We wish to make the ideas and experiments presented in this special 
issue very easily accessible to other researchers. We will therefore 
require all authors to: a) Post their data (training and testing) on the 
web. b) Make their code available in a form that allows other 
researchers to repeat easily the experiments, as well as run the code on 
different data and test modified versions of the algorithms. The form 
(executable, sources, libraries) and level of documentation is up to the 
authors. The editors and the referees are allowed to make use of the 
code and database in their review of the manuscript.

The editors will encourage some of the referees to write a short 
commentary on the paper and on their experience in testing the code. The 
authors will be allowed a rebuttal if appropriate.

As a second category of paper, we solicit submissions describing 
non-proprietary vision databases created for benchmarking or for 
training. Such databases are proving to be crucial for progress in both 
machine learning and computer vision. The creation of a good database 
requires much thought, effort, and care. We want to recognize that 
scientific contribution by assigning the status of a journal paper to a 
good training set or database. We expect that such a paper will describe 
the motivation and intellectual contributions of the database (e.g. by 
comparing with previously available databases and perhaps pointing out 
their shortcomings), as well as details of the collection and labelling. 
The ideal database is one that can be augmented by other researchers.

Submissions should be marked "Special Issue: Learning for Vision" and 
sent to: Monique Fier / IJCV / Springer Monique.Fier@springer-sbm.com, 
+1 781 681 0607 Please indicate who of the three special issue editors 
should handle your paper (try to match the subject areas with the 
expertise of the editor).

We will return without review submissions that we feel are not well 
aligned with our goals for the issue. We will be happy to take a look at 
abstracts and drafts ahead of time, to let you know whether we feel that 
the paper would fit with the issue. In this case, please send your 
material to one of the editors of the special issue ahead of time.

Submission deadline: August 15, 2005

Scheduled publication date: Fall 2006

Editors:

Bill Freeman (billf@mit.edu)

Pietro Perona (perona@caltech.edu)

Bernhard Schlkopf (bernhard.schoelkopf@tuebingen.mpg.de)