ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images
文献类型：期刊论文
| 作者 | Jia, Kui1; Chan, Tsung-Han2; Zeng, Zinan3; Gao, Shenghua4; Wang, Gang5; Zhang, Tianzhu6 ; Ma, Yi4
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| 刊名 | INTERNATIONAL JOURNAL OF COMPUTER VISION
 |
| 出版日期 | 2016-04-01 |
| 卷号 | 117期号:2页码:173-197 |
| 关键词 | Object Matching Feature Correspondence Low-rank Sparsity |
| DOI | 10.1007/s11263-015-0858-1 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | SHAPE ; RECOGNITION ; ALGORITHM ; REPRESENTATION ; REGISTRATION ; CONSTRAINTS |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000372926500005 |
| 资助机构 | National Natural Science Foundation of China(61202158) ; Singapore's Agency for Science, Technology and Research (A*STAR) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12193]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 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 |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：自动化研究所
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