摘要
图像分割是个病态问题,精确化的图像分割需要用户提供足够多的约束信息才能实现.近年来随着马尔可夫随机场吉布斯能量函数最小化图割求解技术的突破,许多国外研究人员开展基于图割方法的交互式图像分割技术的研究.在众多交互式图像分割技术中,由于用户友好性和潜在应用价值,采用矩形框约束的交互式图像分割方法非常吸引人.从超像素马尔可夫随机场模型和网格马尔可夫随机场模型出发,在吉布斯能量函数中引入高阶势能项,高阶势能项的引入使得新的模型既能捕捉细粒度的单个像素信息又能捕捉单像素一定范围内的关联信息,从而提高了矩形框限制条件下的图像分割性能.实验表明:与GrabCut算法相比,所提算法准确性上有一定提高.最后,将所提算法应用在视频对象分割上也取得了不错的效果.
Image segmentation is an ill-posed problem, ana accu,a 6~ ~, only if users supply enough constraint information. And the constraint information is always obtained in a user interactive way, and the foreground and background brush are used to label parts of the image pixels. In recent years, due to great progress in graph cut for solving the Markov random field （MRF） problem, more and more foreign researchers have developed many interactive image segmentation tools based on graph cut. Among all these tools, segmentation from a bounding box method is really attractive for its user-friendliness and perspective application. Based on recent popular grid MRF and SuperPixel MRF model, we introduce the higher order potential to the MRF based image segmentation problem. High order potential can capture not only pixel level image segmentation accuracy, but also local range image pixel correlation information. So the image segmentation algorithm performance is greatly enhanced due to the introduction of high order potential of for Gibbs energy function. Compared with pair-wise term MRF, high order MRF model has higher image description precision. Experimental results on image database show that our method outperforms OrabCut method. Finally, we extend our image segmentation from a box method to the video object segmentation problem and get good results.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2013年第9期1933-1942,共10页
Journal of Computer Research and Development
基金
北京市自然科学基金项目(4122049)
中央高校基本科研业务费青年人才培养基金项目(FRF-TP-12-081A)
