PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning With Constrained and Unlabelled Data
Tilman Lange, Martin H. Law, Anil K. Jain and Joachim Buhmann
In: CVPR 2005, 20-25 June 2005, San Diego, USA.


Classification problems abundantly arise in many computer vision tasks – being of supervised, semi-supervised or unsupervised nature. Even when class labels are not available, a user still might favor certain grouping solutions over others. This bias can be expressed either by providing a clustering criterion or cost function and, in addition to that, by specifying pairwise constraints on the assignment of objects to classes. In this work, we discuss a unifying formulation for labelled and unlabelled data that can incorporate constrained data for model fitting. Our approach models the constraint information by the maximum entropy principle. This modeling strategy allows us (i) to handle constraint violations and soft constraints, and, at the same time, (ii) to speed up the optimization process. Experimental results on face classification and image segmentation indicates that the proposed algorithm is computationally efficient and generates superior groupings when compared with alternative techniques.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:2016
Deposited By:Tilman Lange
Deposited On:15 January 2006