EAGER: Solving Markov Random Fields with Mutual Exclusion Constraints

本项目探索了一种新的方法来增加马尔可夫随机场（MRF）的表达能力，同时提高计算效率和解决方案的质量。研究的重点是利用二次互斥约束（QMC）表示的二次等式形式。目前的方法，以提高表达能力的MRF面临着一个非常具有挑战性的问题，更高的计算时间。相比之下，这种方法能够用QMC来限制解搜索空间，这反过来不仅导致明显更好的解，而且还减少了计算时间。计算机视觉中的许多问题，包括但不限于图像和视频分割，立体和图像恢复，对象检测和识别，跟踪和活动识别，被制定为优化问题，涉及马尔可夫随机场（MRF）的最大后验（MAP）解决方案的推断。QMC比以线性等式形式表示的互斥约束更通用。因此，QMC提供了更强的表达能力，可以更准确地建模许多计算机视觉问题。当一元和二元MRF势是不可靠的和无信息的时，这一性质特别重要，这在真实的应用中是规则而不是例外。因此，本计画可以提升电脑视觉系统的能力，以扩大其应用范围，从影像撷取到移动的机器人上的电脑视觉系统。

This project explores a new way to increase the expressive power of Markov Random Fields (MRF) while at the same time improving the computing efficiency and the quality of solutions. The research focuses on utilizing quadratic mutual exclusion constraints (QMCs) expressed in quadratic equality form. Current approaches to increasing the expressive power of MRFs face a very challenging problem of higher computing time. In contrast, this approach is able to restrict the solution search space with QMCs, which in turn not only leads to significantly better solutions but also to reduced computing time.Many problems in computer vision, including but not limited to image and video segmentation, stereo, and image restoration, object detection and recognition, tracking, and activity recognition, are formulated as optimization problems involving inference of the maximum a posteriori (MAP) solution of a Markov Random Field (MRF). QMCs are more general than mutex constraints expressed in a linear equality form. Hence QMCs offer increased expressive power to more accurately model many computer vision problems. This property is particularly important when unary and binary MRF potentials are unreliable and uninformative, which is the rule rather than an exception in real applications. Hence, this project can increase the ability of computer vision systems to broaden their application scope, ranging from image retrieval to computer vision systems on mobile robots.