E-GrabCut： an economic method of iterative video object extraction

• Published in: Frontiers of Computer Science Vol. 11; no. 4; pp. 649 - 660
• Main Authors: DONG, Le, FENG, Ning, MAO, Mengdie, HE, Ling, WANG, Jingjing
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.08.2017; Springer Nature B.V
• Subjects: Algorithms; Clustering; Computer Science; Efficiency; GMM; GrabCut; interactive video object extraction; Iterative methods; Methods; Probabilistic models; Research Article; Segmentation; video segmentation; 分割技术; 提取; 时间效率; 经济法; 视频分割算法; 视频对象分割算法; 非迭代; 高斯混合模型; GrabCut; video segmentation; interactive video object extraction; GMM
• ISSN: 2095-2228; 2095-2236
• DOI: 10.1007/s11704-016-5558-7

Efficient, interactive foreground/background segmentation in video is of great practical importance in video editing. This paper proposes an interactive and unsupervised video object segmentation algorithm named E-GrabCut concentrating on achieving both of the segmentation quality and time efficiency as highly demanded in the related filed. There are three features in the proposed algorithms. Firstly, we have developed a powerful, non-iterative version of the optimization process for each frame. Secondly, more user interaction in the first frame is used to improve the Gaussian Mixture Model (GMM). Thirdly, a robust algorithm for the following frame segmentation has been developed by reusing the previous GMM. Extensive experiments demonstrate that our method outperforms the state-of-the-art video segmentation algorithm in terms of integration of time efficiency and segmentation quality.