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|Title:||Weighted block-sparse low rank representation for face clustering in videos|
|Citation:||Computer Vision - ECCV 2014, 13th European Conference, Zurich, Switzerland, September 6-12, 2014: Proceedings, Part IV, 2014 / Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (ed./s), vol.8694 LNCS, iss.PART 6, pp.123-138|
|Series/Report no.:||Lecture Notes in Computer Science; 8694|
|Conference Name:||European Conference on Computer Vision (ECCV) (06 Sep 2014 - 12 Sep 2014 : Zurich, Switzerland)|
|Shijie Xiao, Mingkui Tan, and Dong Xu|
|Abstract:||In this paper, we study the problem of face clustering in videos. Specifically, given automatically extracted faces from videos and two kinds of prior knowledge (the face track that each face belongs to, and the pairs of faces that appear in the same frame), the task is to partition the faces into a given number of disjoint groups, such that each group is associated with one subject. To deal with this problem, we propose a new method called weighted block-sparse low rank representation (WBSLRR) which considers the available prior knowledge while learning a low rank data representation, and also develop a simple but effective approach to obtain the clustering result of faces. Moreover, after using several acceleration techniques, our proposed method is suitable for solving large-scale problems. The experimental results on two benchmark datasets demonstrate the effectiveness of our approach.|
|Keywords:||Low rank representation; block-sparsity; subspace clustering; face clustering|
|Rights:||© Springer International Publishing Switzerland 2014|
|Appears in Collections:||Computer Science publications|
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