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Article: An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition

TitleAn Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition
Authors
Keywordsdomain adaptation
Domain generalization
latent domain discovery
multi-view learning
Issue Date2018
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2018, v. 29, n. 2, p. 259-272 How to Cite?
AbstractIn this paper, we propose a new exemplar-based multi-view domain generalization (EMVDG) framework for visual recognition by learning robust classifier that are able to generalize well to arbitrary target domain based on the training samples with multiple types of features (i.e., multi-view features). In this framework, we aim to address two issues simultaneously. First, the distribution of training samples (i.e., the source domain) is often considerably different from that of testing samples (i.e., the target domain), so the performance of the classifiers learnt on the source domain may drop significantly on the target domain. Moreover, the testing data are often unseen during the training procedure. Second, when the training data are associated with multi-view features, the recognition performance can be further improved by exploiting the relation among multiple types of features. To address the first issue, considering that it has been shown that fusing multiple SVM classifiers can enhance the domain generalization ability, we build our EMVDG framework upon exemplar SVMs (ESVMs), in which a set of ESVM classifiers are learnt with each one trained based on one positive training sample and all the negative training samples. When the source domain contains multiple latent domains, the learnt ESVM classifiers are expected to be grouped into multiple clusters. To address the second issue, we propose two approaches under the EMVDG framework based on the consensus principle and the complementary principle, respectively. Specifically, we propose an EMVDGCO method by adding a co-regularizer to enforce the cluster structures of ESVM classifiers on different views to be consistent based on the consensus principle. Inspired by multiple kernel learning, we also propose another EMVDGMK method by fusing the ESVM classifiers from different views based on the complementary principle. In addition, we further extend our EMVDG framework to exemplar-based multi-view domain adaptation (EMVDA) framework when the unlabeled target domain data are available during the training procedure. The effectiveness of our EMVDG and EMVDA frameworks for visual recognition is clearly demonstrated by comprehensive experiments on three benchmark data sets.
Persistent Identifierhttp://hdl.handle.net/10722/321709
ISSN
2023 Impact Factor: 10.2
2023 SCImago Journal Rankings: 4.170
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorNiu, Li-
dc.contributor.authorLi, Wen-
dc.contributor.authorXu, Dong-
dc.contributor.authorCai, Jianfei-
dc.date.accessioned2022-11-03T02:20:56Z-
dc.date.available2022-11-03T02:20:56Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2018, v. 29, n. 2, p. 259-272-
dc.identifier.issn2162-237X-
dc.identifier.urihttp://hdl.handle.net/10722/321709-
dc.description.abstractIn this paper, we propose a new exemplar-based multi-view domain generalization (EMVDG) framework for visual recognition by learning robust classifier that are able to generalize well to arbitrary target domain based on the training samples with multiple types of features (i.e., multi-view features). In this framework, we aim to address two issues simultaneously. First, the distribution of training samples (i.e., the source domain) is often considerably different from that of testing samples (i.e., the target domain), so the performance of the classifiers learnt on the source domain may drop significantly on the target domain. Moreover, the testing data are often unseen during the training procedure. Second, when the training data are associated with multi-view features, the recognition performance can be further improved by exploiting the relation among multiple types of features. To address the first issue, considering that it has been shown that fusing multiple SVM classifiers can enhance the domain generalization ability, we build our EMVDG framework upon exemplar SVMs (ESVMs), in which a set of ESVM classifiers are learnt with each one trained based on one positive training sample and all the negative training samples. When the source domain contains multiple latent domains, the learnt ESVM classifiers are expected to be grouped into multiple clusters. To address the second issue, we propose two approaches under the EMVDG framework based on the consensus principle and the complementary principle, respectively. Specifically, we propose an EMVDGCO method by adding a co-regularizer to enforce the cluster structures of ESVM classifiers on different views to be consistent based on the consensus principle. Inspired by multiple kernel learning, we also propose another EMVDGMK method by fusing the ESVM classifiers from different views based on the complementary principle. In addition, we further extend our EMVDG framework to exemplar-based multi-view domain adaptation (EMVDA) framework when the unlabeled target domain data are available during the training procedure. The effectiveness of our EMVDG and EMVDA frameworks for visual recognition is clearly demonstrated by comprehensive experiments on three benchmark data sets.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systems-
dc.subjectdomain adaptation-
dc.subjectDomain generalization-
dc.subjectlatent domain discovery-
dc.subjectmulti-view learning-
dc.titleAn Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TNNLS.2016.2615469-
dc.identifier.pmid27834652-
dc.identifier.scopuseid_2-s2.0-84995447186-
dc.identifier.volume29-
dc.identifier.issue2-
dc.identifier.spage259-
dc.identifier.epage272-
dc.identifier.eissn2162-2388-
dc.identifier.isiWOS:000422952400002-

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