CASIA OpenIR  > 多媒体计算与图形学团队
Interactive stereo image segmentation via adaptive prior selection
Ma, Wei1; Qin, Yue1; Xu, Shibiao2; Zhang, Xiaopeng2
Source PublicationMULTIMEDIA TOOLS AND APPLICATIONS
ISSN1380-7501
2018-11-01
Volume77Issue:21Pages:28709-28724
Corresponding AuthorXu, Shibiao(shibiao.xu@ia.ac.cn) ; Zhang, Xiaopeng(xiaopeng.zhang@ia.ac.cn)
AbstractInteractive stereo image segmentation (i.e., cutting out objects from stereo pairs with limited user assistance) is an important research topic in computer vision. Given a pair of images, users mark a few foreground/background pixels, based on which prior models are formulated for labeling unknown pixels. Note that color priors might not help if the marked foreground and background have similar colors. However, integrating multiple types of priors, e.g., color and disparity in segmenting stereo pairs, is not trivial. This is because differing pairs of images and even differing pixels in the same image might require different proportions of the priors. Besides, disparities of natural images are too noisy to be directly used. This paper presents a method that can adaptively determine the proportion of the priors (color or disparity) for each pixel. Specifically speaking, the segmentation problem is defined in the framework of MRF (Markov Random Field). We formulate an MRF energy function which is composed of clues from the two types of priors, as well as neighborhood smoothness and stereo correspondence constraints. The weights of the color and disparity priors at each pixel are treated as variables which are optimized together with the label (foreground or background) of the pixel. In order to overcome the noise problem, the weight of the disparity prior is controlled by a confidence value learned from data. The energy function is optimized by using multi-label graph cut. Experimental results show that our method performs well.
KeywordStereo image segmentation Interactive segmentation Prior selection Multi-label MRF Graph cut
DOI10.1007/s11042-018-6067-5
WOS KeywordGRAPH CUTS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61771026] ; National Natural Science Foundation of China[61379096] ; National Natural Science Foundation of China[61671451] ; National Natural Science Foundation of China[61502490] ; Scientific Research Project of Beijing Educational Committee[KM201510005015] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)[4152006] ; Beijing Municipal Natural Science Foundation[4152006]
Funding OrganizationNational Natural Science Foundation of China ; Scientific Research Project of Beijing Educational Committee ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; Beijing Municipal Natural Science Foundation
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000446601500038
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23050
Collection多媒体计算与图形学团队
Corresponding AuthorXu, Shibiao; Zhang, Xiaopeng
Affiliation1.Beijing Univ Technol, Fac Informat Technol, 100 Pingleyuan St, Beijing 100124, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Ma, Wei,Qin, Yue,Xu, Shibiao,et al. Interactive stereo image segmentation via adaptive prior selection[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2018,77(21):28709-28724.
APA Ma, Wei,Qin, Yue,Xu, Shibiao,&Zhang, Xiaopeng.(2018).Interactive stereo image segmentation via adaptive prior selection.MULTIMEDIA TOOLS AND APPLICATIONS,77(21),28709-28724.
MLA Ma, Wei,et al."Interactive stereo image segmentation via adaptive prior selection".MULTIMEDIA TOOLS AND APPLICATIONS 77.21(2018):28709-28724.
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