CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Gong, Yongchao; Xiang, Shiming; Pan, Chunhong
Conference NameIEEE International Conference on Acoustics, Speech and Signal Processing
Conference Date2016-3-20 ~ 2016-3-25
Conference PlaceShanghai, China
AbstractRandom 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.
KeywordRandom Walks Data-guided Random Walks Fine-structured Object Segmentation Labeling Preference
Indexed ByEI
Document Type会议论文
AffiliationNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Gong, Yongchao,Xiang, Shiming,Pan, Chunhong. DATA-GUIDED RANDOM WALKS FOR FINE-STRUCTURED OBJECT SEGMENTATION[C],2016:1806-1810.
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