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Joint Alignment and Clustering via Low-Rank Representation
Qi Li; Zhenan Sun; Ran He(赫然); Tieniu Tan; Li, Qi
2013-11
会议名称IAPR Asian Conference on Pattern Recognition
会议录名称Asian Conference on Pattern Recognition
会议日期2013年11月5-8日
会议地点Naha, Japan
摘要
Both image alignment and image clustering are widely researched with numerous applications in recent years. These two problems are traditionally studied separately. However in many real world applications, both alignment and clustering results are needed. Recent study has shown that alignment and clustering are two highly coupled problems. Thus we try to solve the two problems in a unified framework. In this paper, we propose a novel joint alignment and clustering algorithm by integrating spatial transformation parameters and clustering parameters into a unified objective function. The proposed function seeks the lowest rank representation among all the candidates that can represent misaligned images. It is indeed a transformed Low-Rank Representation. As far as we know, this is the first time to cluster the misaligned images using the transformed Low-Rank Representation. We can solve the proposed function by linearizing the objective function, and then iteratively solving a sequence of linear problems via the Augmented Lagrange Multipliers method. Experimental results on various data sets validate the effectiveness of our method.
关键词Joint Alignment And Clustering Low-rank Representation Augmented Lagrange Multiplier Method
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11678
专题智能感知与计算研究中心
通讯作者Li, Qi
作者单位Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
推荐引用方式
GB/T 7714
Qi Li,Zhenan Sun,Ran He,et al. Joint Alignment and Clustering via Low-Rank Representation[C],2013.
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