Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Linear dependency modeling for feature fusion

Language

English

Abstract

This paper addresses the independent assumption issue in fusion process. In the last decade, dependency modeling techniques were developed under a specific distribution of classifiers. This paper proposes a new framework to model the dependency between features without any assumption on feature/classifier distribution. In this paper, we prove that feature dependency can be modeled by a linear combination of the posterior probabilities under some mild assumptions. Based on the linear combination property, two methods, namely Linear Classifier Dependency Modeling (LCDM) and Linear Feature Dependency Modeling (LFDM), are derived and developed for dependency modeling in classifier level and feature level, respectively. The optimal models for LCDM and LFDM are learned by maximizing the margin between the genuine and imposter posterior probabilities. Both synthetic data and real datasets are used for experiments. Experimental results show that LFDM outperforms all existing combination methods. © 2011 IEEE.

Publication Date

2011

Source Publication Title

2011 IEEE International Conference on Computer Vision

Start Page

2041

End Page

2048

Conference Location

Barcelona, Spain

Publisher

IEEE

DOI

10.1109/ICCV.2011.6126477

Link to Publisher's Edition

http://dx.doi.org/10.1109/ICCV.2011.6126477

ISBN (print)

9781457711015

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