CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
FINE-STRUCTURED OBJECT SEGMENTATION VIA EDGE-GUIDED GRAPH CUT WITH INTERACTION SIMPLIFICATION
Gong, Yongchao; Xiang, Shiming; Wang, Lingfeng; Pan, Chunhong
2016-03
Conference NameIEEE International Conference on Acoustics, Speech and Signal Processing
Pages1801-1805
Conference Date2016-3-20 ~ 2016-3-25
Conference PlaceShanghai, China
AbstractFine-structured object segmentation is a challenging problem in object segmentation community. There are mainly two difficulties that can seriously degrade the segmentation quality: 1) insufficient interactions on fine structures due to the high demand of time and manual efforts, and 2) shrinking bias that discourages long object boundaries. To address these two issues, we develop a novel method within the graph cut framework. First, the commonly used operation of scribbling or dragging bounding boxes is replaced by loosely drawing a few rectangles, thus the interaction burden is largely reduced. Second, an edge-guided graph cut model is proposed to mitigate shrinking bias. This model enforces connectivity of fine structures by adjusting the weighting between neighboring pixels. Finally, the segmentation task is formulated as an optimization problem, which can be optimized effectively and efficiently. Comparative experimental results demonstrate the effectiveness of our method.
KeywordFine-structured Object Segmentation Interaction Simplification Edge-guided Graph Cut
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14491
Collection模式识别国家重点实验室_先进数据分析与学习
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,Wang, Lingfeng,et al. FINE-STRUCTURED OBJECT SEGMENTATION VIA EDGE-GUIDED GRAPH CUT WITH INTERACTION SIMPLIFICATION[C],2016:1801-1805.
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