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论文类型：期刊论文

发表时间：2016-12-01

发表刊物：IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

收录刊物：SCIE、EI、Scopus

卷号：38

期号：12

页面范围：2457-2471

ISSN号：0162-8828

关键字：Visual diffusion; PDE governed combinatorial optimization; submodularity; saliency detection; object tracking

摘要：Partial differential equations (PDEs) have been used to formulate image processing for several decades. Generally, a PDE system consists of two components: the governing equation and the boundary condition. In most previous work, both of them are generally designed by people using mathematical skills. However, in real world visual analysis tasks, such predefined and fixed-form PDEs may not be able to describe the complex structure of the visual data. More importantly, it is hard to incorporate the labeling information and the discriminative distribution priors into these PDEs. To address above issues, we propose a new PDE framework, named learning to diffuse (LTD), to adaptively design the governing equation and the boundary condition of a diffusion PDE system for various vision tasks on different types of visual data. To our best knowledge, the problems considered in this paper (i.e., saliency detection and object tracking) have never been addressed by PDE models before. Experimental results on various challenging benchmark databases show the superiority of LTD against existing state-of-the-art methods for all the tested visual analysis tasks.