http://dx.doi.org/10.1007/978-3-642-33712-3_57">
 

Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Reduced analytical dependency modeling for classifier fusion

Language

English

Abstract

This paper addresses the independent assumption issue in classifier fusion process. In the last decade, dependency modeling techniques were developed under some specific assumptions which may not be valid in practical applications. In this paper, using analytical functions on posterior probabilities of each feature, we propose a new framework to model dependency without those assumptions. With the analytical dependency model (ADM), we give an equivalent condition to the independent assumption from the properties of marginal distributions, and show that the proposed ADM can model dependency. Since ADM may contain infinite number of undetermined coefficients, we further propose a reduced form of ADM, based on the convergent properties of analytical functions. Finally, under the regularized least square criterion, an optimal Reduced Analytical Dependency Model (RADM) is learned by approximating posterior probabilities such that all training samples are correctly classified. Experimental results show that the proposed RADM outperforms existing classifier fusion methods on Digit, Flower, Face and Human Action databases. © 2012 Springer-Verlag.

Keywords

analytical function, classifier fusion, Dependency modeling, pattern classification

Publication Date

2012

Source Publication Title

Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part III

Start Page

792

End Page

805

Conference Location

Florence, Italy

Publisher

Springer

ISBN (print)

9783642337116

ISBN (electronic)

9783642337123

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