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- Publisher Website: 10.1109/TNNLS.2014.2386307
- Scopus: eid_2-s2.0-84943742851
- PMID: 25616081
- WOS: WOS:000362358800018
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Article: Automatic Face Naming by Learning Discriminative Affinity Matrices from Weakly Labeled Images
Title | Automatic Face Naming by Learning Discriminative Affinity Matrices from Weakly Labeled Images |
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Authors | |
Keywords | Affinity matrix caption-based face naming distance metric learning low-rank representation (LRR) |
Issue Date | 2015 |
Citation | IEEE Transactions on Neural Networks and Learning Systems, 2015, v. 26, n. 10, p. 2440-2452 How to Cite? |
Abstract | Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We first propose a new method called regularized low-rank representation by effectively utilizing weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. Specifically, by introducing a specially designed regularizer to the low-rank representation method, we penalize the corresponding reconstruction coefficients related to the situations where a face is reconstructed by using face images from other subjects or by using itself. With the inferred reconstruction coefficient matrix, a discriminative affinity matrix can be obtained. Moreover, we also develop a new distance metric learning method called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. Hence, another discriminative affinity matrix can be obtained using the similarity matrix (i.e., the kernel matrix) based on the Mahalanobis distances of the data. Observing that these two affinity matrices contain complementary information, we further combine them to obtain a fused affinity matrix, based on which we develop a new iterative scheme to infer the name of each face. Comprehensive experiments demonstrate the effectiveness of our approach. |
Persistent Identifier | http://hdl.handle.net/10722/321647 |
ISSN | 2023 Impact Factor: 10.2 2023 SCImago Journal Rankings: 4.170 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xiao, Shijie | - |
dc.contributor.author | Xu, Dong | - |
dc.contributor.author | Wu, Jianxin | - |
dc.date.accessioned | 2022-11-03T02:20:28Z | - |
dc.date.available | 2022-11-03T02:20:28Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 2015, v. 26, n. 10, p. 2440-2452 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | http://hdl.handle.net/10722/321647 | - |
dc.description.abstract | Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We first propose a new method called regularized low-rank representation by effectively utilizing weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. Specifically, by introducing a specially designed regularizer to the low-rank representation method, we penalize the corresponding reconstruction coefficients related to the situations where a face is reconstructed by using face images from other subjects or by using itself. With the inferred reconstruction coefficient matrix, a discriminative affinity matrix can be obtained. Moreover, we also develop a new distance metric learning method called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. Hence, another discriminative affinity matrix can be obtained using the similarity matrix (i.e., the kernel matrix) based on the Mahalanobis distances of the data. Observing that these two affinity matrices contain complementary information, we further combine them to obtain a fused affinity matrix, based on which we develop a new iterative scheme to infer the name of each face. Comprehensive experiments demonstrate the effectiveness of our approach. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Neural Networks and Learning Systems | - |
dc.subject | Affinity matrix | - |
dc.subject | caption-based face naming | - |
dc.subject | distance metric learning | - |
dc.subject | low-rank representation (LRR) | - |
dc.title | Automatic Face Naming by Learning Discriminative Affinity Matrices from Weakly Labeled Images | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TNNLS.2014.2386307 | - |
dc.identifier.pmid | 25616081 | - |
dc.identifier.scopus | eid_2-s2.0-84943742851 | - |
dc.identifier.volume | 26 | - |
dc.identifier.issue | 10 | - |
dc.identifier.spage | 2440 | - |
dc.identifier.epage | 2452 | - |
dc.identifier.eissn | 2162-2388 | - |
dc.identifier.isi | WOS:000362358800018 | - |