Salient Object Segmentation via Effective Integration of Saliency and Objectness
Salient Object Segmentation via Effective Integration of Saliency and Objectness
复制标题
通过有效整合显着性和对象性进行显着对象分割
DOI：
10.1109/tmm.2017.2693022
复制
发表时间：
2017
影响因子：
7.3
通讯作者：
Wang Yang
中科院分区：
文献类型：
--
作者：
Ye Linwei;Liu Zhi;Li Lina;Shen Liquan;Bai Cong;Wang Yang
This paper proposes an effective salient object segmentation method via the graph-based integration of saliency and objectness. Based on the superpixel segmentation result of the input image, a graph is built to represent superpixels using regular vertex, background seed vertex with the addition of a terminal vertex. The edge weights on the graph are defined by integrating the difference of appearance, saliency, and objectness between superpixels. Then, the object probability of each superpixel is measured by finding the shortest path from the corresponding vertex to the terminal vertex on the graph, and the resultant object probability map can generally better highlight salient objects and suppress background regions compared to both saliency map and objectness map. Finally, the object probability map is used to initialize salient object and background, and effectively incorporated into the framework of graph cut to obtain the final salient object segmentation result. Extensive experimental results on three public benchmark datasets show that the proposed method consistently improves the salient object segmentation performance and outperforms the state-of-the-art salient object segmentation methods. Furthermore, experimental results also demonstrate that the proposed graph-based integration method is more effective than other fusion schemes and robust to saliency maps generated using various saliency models.
