一种基于交替凸优化的视频对象分割算法

A Video Object Segmentation Algorithm Based on Alternation Convex Optimization

现有视频对象分割方案多数存在应用场景受限、运动背景过分割等问题,为此,提出一种可从视频序列中自动检测重要对象的无监督视频对象分割算法。从前景和背景概率分布的角度引入马尔可夫能量、时空能量和对抗能量。将视频对象分割问题建模为基于3种混合能量最小化的非凸优化问题,利用基于交替凸优化的方法将其分解为2个二次规划问题。采用前向-反向传递策略,以充分利用时域相关性从而提高对象分割的可靠性。结合多种视频数据集进行仿真,结果表明,与其他最新的视频对象分割算法相比,该算法的分割性能有明显提高。

There are some deficiencies while using existing schemes,such as limited application scenes and over segmentation of motion background.An unsupervised video object segmentation algorithm is proposed,which can automatically detect important objects from video sequences.Markov energy,time and space energy,as well as antagonism energy are introduced from the view of the foreground and background probability distribution.Then,the problem of detecting important objects from the background is modeled as a non convex optimization problem based on the mixed energy minimization,and a method based on Alternation Convex Optimization(ACO)is proposed to decompose the problem into two kinds of two quadratic programming problems.In order to make full use of time-domain correlation to improve the reliability of object segmentation,a forward-backward deliver strategy is also adopted.A comprehensive simulation is carried out based on a variety of video datasets.Experimental results show that the performance of the algorithm in this paper is significantly better than the other latest video object segmentation algorithms.