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Conference Paper: Weighted block-sparse low rank representation for face clustering in videos
Title | Weighted block-sparse low rank representation for face clustering in videos |
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Authors | |
Keywords | block-sparsity face clustering low rank representation subspace clustering |
Issue Date | 2014 |
Publisher | Springer |
Citation | 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, 6-12 September 2014. In Fleet, D, Pajdla, T, Schiele, B, et al. (Eds.), Computer Vision -- ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI, p. 123-138. Cham: Springer, 2014 How to Cite? |
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. © 2014 Springer International Publishing. |
Persistent Identifier | http://hdl.handle.net/10722/321608 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
Series/Report no. | Lecture Notes in Computer Science ; 8694 LNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics |
DC Field | Value | Language |
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dc.contributor.author | Xiao, Shijie | - |
dc.contributor.author | Tan, Mingkui | - |
dc.contributor.author | Xu, Dong | - |
dc.date.accessioned | 2022-11-03T02:20:12Z | - |
dc.date.available | 2022-11-03T02:20:12Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, 6-12 September 2014. In Fleet, D, Pajdla, T, Schiele, B, et al. (Eds.), Computer Vision -- ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI, p. 123-138. Cham: Springer, 2014 | - |
dc.identifier.isbn | 9783319105987 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321608 | - |
dc.description.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. © 2014 Springer International Publishing. | - |
dc.language | eng | - |
dc.publisher | Springer | - |
dc.relation.ispartof | Computer Vision -- ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; 8694 | - |
dc.relation.ispartofseries | LNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics | - |
dc.subject | block-sparsity | - |
dc.subject | face clustering | - |
dc.subject | low rank representation | - |
dc.subject | subspace clustering | - |
dc.title | Weighted block-sparse low rank representation for face clustering in videos | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-319-10599-4_9 | - |
dc.identifier.scopus | eid_2-s2.0-84906352140 | - |
dc.identifier.spage | 123 | - |
dc.identifier.epage | 138 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.publisher.place | Cham | - |