DATA-GUIDED RANDOM WALKS FOR FINE-STRUCTURED OBJECT SEGMENTATION
文献类型：会议论文
| 作者 | Gong, Yongchao ; Xiang, Shiming; Pan, Chunhong
|
| 出版日期 | 2016-03 |
| 会议日期 | 2016-3-20 ~ 2016-3-25 |
| 会议地点 | Shanghai, China |
| 关键词 | Random Walks Data-guided Random Walks Fine-structured Object Segmentation Labeling Preference |
| 页码 | 1806-1810 |
| 英文摘要 | Random walks (RW) is a popular technique for object segmentation. Apart from the satisfactory performance in various applications, its most appealing advantage is the computational efficiency. However, RW often fails to produce complete and connected results in finestructured (FS) object segmentation. To utilize the high efficiency and overcome the drawbacks in tackling FS objects, we develop a novel approach within the RW framework. Specifically, we propose to introduce labeling preference learned from the image data into the RW model to guide the propagation of random walkers. With the help of the guidance, random walkers are more likely to propagate correctly to the FS regions, thus yielding more accurate results. Similar to RW, this approach also bears properties such as computational efficiency, closed-form solution and unique global optimum. Moreover, it has the capacities of handling disconnected objects and transferring segmentation. Comparative experimental results demonstrate that the proposed approach achieves the state-of-the-art performance in FS object segmentation, with a low requirement of runtime. |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14492]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 作者单位 | National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Gong, Yongchao,Xiang, Shiming,Pan, Chunhong. DATA-GUIDED RANDOM WALKS FOR FINE-STRUCTURED OBJECT SEGMENTATION[C]. 见:. Shanghai, China. 2016-3-20 ~ 2016-3-25. |
入库方式： OAI收割
来源：自动化研究所
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