File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Sparse label-indicator optimization methods for image classification

TitleSparse label-indicator optimization methods for image classification
Authors
Keywordsimage classification
sparsity
semi-supervised learning
multi-class
Graph
random walk with restart
Issue Date2014
Citation
IEEE Transactions on Image Processing, 2014, v. 23, n. 3, p. 1002-1014 How to Cite?
AbstractImage label prediction is a critical issue in computer vision and machine learning. In this paper, we propose and develop sparse label-indicator optimization methods for image classification problems. Sparsity is introduced in the label-indicator such that relevant and irrelevant images with respect to a given class can be distinguished. Also, when we deal with multi-class image classification problems, the number of possible classes of a given image can also be constrained to be small in which it is valid for natural images. The resulting sparsity model can be formulated as a convex optimization problem, and it can be solved very efficiently. Experimental results are reported to illustrate the effectiveness of the proposed model, and demonstrate that the classification performance of the proposed method is better than the other testing methods in this paper. © 2014 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/276979
ISSN
2020 Impact Factor: 10.856
2020 SCImago Journal Rankings: 1.778
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJing, Liping-
dc.contributor.authorNg, Michael K.-
dc.date.accessioned2019-09-18T08:35:14Z-
dc.date.available2019-09-18T08:35:14Z-
dc.date.issued2014-
dc.identifier.citationIEEE Transactions on Image Processing, 2014, v. 23, n. 3, p. 1002-1014-
dc.identifier.issn1057-7149-
dc.identifier.urihttp://hdl.handle.net/10722/276979-
dc.description.abstractImage label prediction is a critical issue in computer vision and machine learning. In this paper, we propose and develop sparse label-indicator optimization methods for image classification problems. Sparsity is introduced in the label-indicator such that relevant and irrelevant images with respect to a given class can be distinguished. Also, when we deal with multi-class image classification problems, the number of possible classes of a given image can also be constrained to be small in which it is valid for natural images. The resulting sparsity model can be formulated as a convex optimization problem, and it can be solved very efficiently. Experimental results are reported to illustrate the effectiveness of the proposed model, and demonstrate that the classification performance of the proposed method is better than the other testing methods in this paper. © 2014 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Image Processing-
dc.subjectimage classification-
dc.subjectsparsity-
dc.subjectsemi-supervised learning-
dc.subjectmulti-class-
dc.subjectGraph-
dc.subjectrandom walk with restart-
dc.titleSparse label-indicator optimization methods for image classification-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TIP.2013.2294546-
dc.identifier.pmid24474370-
dc.identifier.scopuseid_2-s2.0-84893836262-
dc.identifier.volume23-
dc.identifier.issue3-
dc.identifier.spage1002-
dc.identifier.epage1014-
dc.identifier.isiWOS:000331196900001-
dc.identifier.issnl1057-7149-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats