Learning to Diffuse: A New Perspective to Design PDEs for Visual Analysis.

Abstract

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.