Reduced analytic dependency modeling: Robust fusion for visual recognition

doi:10.1007/s11263-014-0723-7

科研成果详情

题名	Reduced analytic dependency modeling: Robust fusion for visual recognition
作者	Ma，Andy J.1; Yuen，Pong C.1,2
发表日期	2014
发表期刊	International Journal of Computer Vision
ISSN/eISSN	0920-5691
卷号	109 期号:3 页码:233-251
摘要	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.
关键词	Dependency modeling Probabilistic constraints Robustness Score-level fusion Visual recognition
DOI	10.1007/s11263-014-0723-7
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-84905653531
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/6518
专题	北师香港浸会大学
作者单位	1.Department of Computer Science, Hong Kong Baptist University,Kowloon,Hong Kong 2.BNU-HKBU United International College,Zhuhai,China
推荐引用方式 GB/T 7714	Ma，Andy J.,Yuen，Pong C. Reduced analytic dependency modeling: Robust fusion for visual recognition[J]. International Journal of Computer Vision, 2014, 109(3): 233-251.
APA	Ma，Andy J., & Yuen，Pong C. (2014). Reduced analytic dependency modeling: Robust fusion for visual recognition. International Journal of Computer Vision, 109(3), 233-251.
MLA	Ma，Andy J.,et al."Reduced analytic dependency modeling: Robust fusion for visual recognition". International Journal of Computer Vision 109.3(2014): 233-251.