ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images
Jia, Kui1; Chan, Tsung-Han2; Zeng, Zinan3; Gao, Shenghua4; Wang, Gang5; Zhang, Tianzhu6; Ma, Yi4
2016-04-01
发表期刊INTERNATIONAL JOURNAL OF COMPUTER VISION
卷号117期号:2页码:173-197
文章类型Article
摘要Feature-based object matching is a fundamental problem for many applications in computer vision, such as object recognition, 3D reconstruction, tracking, and motion segmentation. In this work, we consider simultaneously matching object instances in a set of images, where both inlier and outlier features are extracted. The task is to identify the inlier features and establish their consistent correspondences across the image set. This is a challenging combinatorial problem, and the problem complexity grows exponentially with the image number. To this end, we propose a novel framework, termed Robust Object Matching using Low-rank constraint (ROML), to address this problem. ROML optimizes simultaneously a partial permutation matrix (PPM) for each image, and feature correspondences are established by the obtained PPMs. Two of our key contributions are summarized as follows. (1) We formulate the problem as rank and sparsity minimization for PPM optimization, and treat simultaneous optimization of multiple PPMs as a regularized consensus problem in the context of distributed optimization. (2) We use the alternating direction method of multipliers method to solve the thus formulated ROML problem, in which a subproblem associated with a single PPM optimization appears to be a difficult integer quadratic program (IQP). We prove that under wildly applicable conditions, this IQP is equivalent to a linear sum assignment problem, which can be efficiently solved to an exact solution. Extensive experiments on rigid/non-rigid object matching, matching instances of a common object category, and common object localization show the efficacy of our proposed method.
关键词Object Matching Feature Correspondence Low-rank Sparsity
WOS标题词Science & Technology ; Technology
DOI10.1007/s11263-015-0858-1
关键词[WOS]SHAPE ; RECOGNITION ; ALGORITHM ; REPRESENTATION ; REGISTRATION ; CONSTRAINTS
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61202158) ; Singapore's Agency for Science, Technology and Research (A*STAR)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000372926500005
引用统计
被引频次:5[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12193
专题模式识别国家重点实验室_多媒体计算与图形学
作者单位1.Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, E11 Ave Univ, Taipa, Macau Sar, Peoples R China
2.MediaTek Inc, 1,Dusing 1st Rd,Hsinchu Sci Pk, Hsinchu 30078, Taiwan
3.Adv Digital Sci Ctr, 1 Fusionopolis Way, Singapore, Singapore
4.ShanghaiTech Univ, Sch Informat Sci & Technol, 8 Bldg,319 Yueyang Rd, Shanghai 200031, Peoples R China
5.Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
6.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
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Jia, Kui,Chan, Tsung-Han,Zeng, Zinan,et al. ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2016,117(2):173-197.
APA Jia, Kui.,Chan, Tsung-Han.,Zeng, Zinan.,Gao, Shenghua.,Wang, Gang.,...&Ma, Yi.(2016).ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images.INTERNATIONAL JOURNAL OF COMPUTER VISION,117(2),173-197.
MLA Jia, Kui,et al."ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images".INTERNATIONAL JOURNAL OF COMPUTER VISION 117.2(2016):173-197.
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