http://dx.doi.org/10.1007/s11263-014-0723-7">
 

Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Reduced analytic dependency modeling: Robust fusion for visual recognition

Language

English

Abstract

This paper addresses the robustness issue of information fusion for visual recognition. Analyzing limitations in existing fusion methods, we discover two key factors affecting the performance and robustness of a fusion model under different data distributions, namely (1) data dependency and (2) fusion assumption on posterior distribution. Considering these two factors, we develop a new framework to model dependency based on probabilistic properties of posteriors without any assumption on the data distribution. Making use of the range characteristics of posteriors, the fusion model is formulated as an analytic function multiplied by a constant with respect to the class label. With the analytic fusion model, we give an equivalent condition to the independent assumption and derive the dependency model from the marginal distribution property. Since the number of terms in the dependency model increases exponentially, the Reduced Analytic Dependency Model (RADM) is proposed based on the convergent property of analytic function. Finally, the optimal coefficients in the RADM are learned by incorporating label information from training data to minimize the empirical classification error under regularized least square criterion, which ensures the discriminative power. Experimental results from robust non-parametric statistical tests show that the proposed RADM method statistically significantly outperforms eight state-of-the-art score-level fusion methods on eight image/video datasets for different tasks of digit, flower, face, human action, object, and consumer video recognition. © 2014 Springer Science+Business Media New York.

Keywords

Dependency modeling, Probabilistic constraints, Robustness, Score-level fusion, Visual recognition

Publication Date

2014

Source Publication Title

International Journal of Computer Vision

Volume

109

Issue

3

Start Page

233

End Page

251

Publisher

Springer Verlag

ISSN (print)

09205691

ISSN (electronic)

15731405

This document is currently not available here.

Share

COinS