Joint Alignment and Clustering via Low-Rank Representation
Qi Li, Zhenan Sun, Ran He, Tieniu Tan · 2013
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 linear zing 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.