Paper Info
Title
Automatic Foreground Extraction using Multi-Agent Consensus Equilibrium.
Abstract
While foreground extraction is fundamental to virtual reality systems and has been studied for decades, majority of the professional softwares today still rely substantially on human interventions, e.g., providing trimaps or labeling key frames. This is not only time consuming, but is also sensitive to human error. In this paper, we present a fully automatic foreground extraction algorithm which does not require any trimap or scribble. Our solution is based on a newly developed concept called the Multi-Agent Consensus Equilibrium (MACE), a framework which allows us to integrate multiple sources of expertise to produce an overall superior result. The MACE framework consists of three agents: (1) A new dual layer closed-form matting agent to estimate the foreground mask using the color image and a background image; (2) A background probability estimator using color difference and object segmentation; (3) A total variation minimization agent to control the smoothness of the foreground masks. We show how these agents are constructed, and how their interactions lead to better performance. We evaluate the performance of the proposed algorithm by comparing to several state-of-the-art methods. On the real datasets we tested, our results show less error compared to the other methods.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Computer vision,Virtual reality,Segmentation,Extraction algorithm,Computer science,Human error,Artificial intelligence,Smoothness,Color difference,Estimator,Color image
DocType
Volume
Citations 
Journal
abs/1808.08210
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Xiran Wang100.68
Jason Juang2251.77
Stanley H. Chan340330.95