Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96254
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dc.contributor.authorChen, Linen
dc.contributor.authorXu, Dongen
dc.contributor.authorTsang, Ivor Wai-Hungen
dc.contributor.authorLuo, Jieboen
dc.date.accessioned2013-07-15T08:50:15Zen
dc.date.accessioned2019-12-06T19:27:54Z-
dc.date.available2013-07-15T08:50:15Zen
dc.date.available2019-12-06T19:27:54Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationChen, L., Xu, D., Tsang, I. W., & Luo, J. (2012). Tag-Based Image Retrieval Improved by Augmented Features and Group-Based Refinement. IEEE Transactions on Multimedia, 14(4), 1057-1067.en
dc.identifier.issn1520-9210en
dc.identifier.urihttps://hdl.handle.net/10356/96254-
dc.identifier.urihttp://hdl.handle.net/10220/11473en
dc.description.abstractIn this paper, we propose a new tag-based image retrieval framework to improve the retrieval performance of a group of related personal images captured by the same user within a short period of an event by leveraging millions of training web images and their associated rich textual descriptions. For any given query tag (e.g., “car”), the inverted file method is employed to automatically determine the relevant training web images that are associated with the query tag and the irrelevant training web images that are not associated with the query tag. Using these relevant and irrelevant web images as positive and negative training data respectively, we propose a new classification method called support vector machine (SVM) with augmented features (AFSVM) to learn an adapted classifier by leveraging the prelearned SVM classifiers of popular tags that are associated with a large number of relevant training web images. Treating the decision values of one group of test photos from AFSVM classifiers as the initial relevance scores, in the subsequent group-based refinement process, we propose to use the Laplacian regularized least squares method to further refine the relevance scores of test photos by utilizing the visual similarity of the images within the group. Based on the refined relevance scores, our proposed framework can be readily applied to tag-based image retrieval for a group of raw consumer photos without any textual descriptions or a group of Flickr photos with noisy tags. Moreover, we propose a new method to better calculate the relevance scores for Flickr photos. Extensive experiments on two datasets demonstrate the effectiveness of our framework.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE transactions on multimediaen
dc.rights© 2012 IEEE.en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleTag-based image retrieval improved by augmented features and group-based refinementen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
dc.contributor.researchCentre for Multimedia and Network Technologyen
dc.identifier.doihttp://dx.doi.org/10.1109/TMM.2012.2187435en
item.grantfulltextnone-
item.fulltextNo Fulltext-
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