PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Partitioning of Image Datasets using Discriminative Context Information
Christoph Lampert
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 24-26 Jun 2008, Anchorage, USA.


We propose a new method to partition an unlabeled dataset, called Discriminative Context Partitioning (DCP). It is motivated by the idea of splitting the dataset based only on how well the resulting parts can be separated from a context class of disjoint data points. This is in contrast to typical clustering techniques like K-means that are based on a generative model by implicitly or explicitly searching for modes in the distribution of samples. The discriminative criterion in DCP avoids the problems that density based methods have when the a priori assumption of multimodality is violated, when the number of samples becomes small in relation to the dimensional- ity of the feature space, or if the cluster sizes are strongly unbalanced. We formulate DCP's separation property as a large-margin criterion, and show how the resulting optimization problem can be solved efficiently. Experiments on the MNIST and USPS datasets of handwritten digits and on a subset of the Caltech256 dataset show that, given a suitable context, DCP can achieve good results even in situation where density-based clustering techniques fail.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4801
Deposited By:Christoph Lampert
Deposited On:24 March 2009