When

Start: 11/07/2012 - 1:15pm

End : 11/07/2012 - 2:15pm

End : 11/07/2012 - 2:15pm

Category

Applied Math Seminar

Speaker

Cristina Garcia Cardona (CGU,SDSU)

Abstract

We propose an extension of a binary diffuse interface model for graph

segmentation to the case of multiple classes. The original binary

diffuse interface model adapts the Ginzburg-Landau energy functional

to a semi-supervised setup on graphs. The graph structure is used to

encode a measure of similarity between data points. A small sample of

labeled data points (semi-supervised) serves as seeds from which label

information can be propagated throughout the graph structure. In this

way, the problem can be posed as a function estimation over the

vertices of the graph (learning on graphs) with the Ginzburg-Landau

energy providing a framework to evaluate the quality of data

segmentation. The multiclass extension modifies this Ginzburg-Landau

energy functional to remove the prejudicial effect that the order of

the labelings, given by integer values, may have in the smoothing term

of the diffuse interface model. We show that the new formulation can

be used to obtain a simultaneous segmentation into several classes and

evaluate its performance in synthetic as well as real data sets. We

discuss practical aspects to improve the performance of the model and

delineate future work.

Where

KRV 164

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