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- Publisher Website: 10.1016/j.imavis.2016.03.005
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Article: A discriminative and sparse topic model for image classification and annotation
Title | A discriminative and sparse topic model for image classification and annotation |
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Authors | |
Keywords | Image classification Discriminative topic Graphical model Image annotation Sparsity |
Issue Date | 2016 |
Citation | Image and Vision Computing, 2016, v. 51, p. 22-35 How to Cite? |
Abstract | © 2016 Elsevier B.V. All rights reserved. Image classification is to assign a category of an image and image annotation is to describe individual components of an image by using some annotation terms. These two learning tasks are strongly related. The main contribution of this paper is to propose a new discriminative and sparse topic model (DSTM) for image classification and annotation by combining visual, annotation and label information from a set of training images. The essential features of DSTM different from existing approaches are that (i) the label information is enforced in the generation of both visual words and annotation terms such that each generative latent topic corresponds to a category; (ii) the zero-mean Laplace distribution is employed to give a sparse representation of images in visual words and annotation terms such that relevant words and terms are associated with latent topics. Experimental results demonstrate that the proposed method provides the discrimination ability in classification and annotation, and its performance is better than the other testing methods (sLDA-ann, abc-corr-LDA, SupDocNADE, SAGE and MedSTC) for LabelMe, UIUC, NUS-WIDE and PascalVOC07 images. |
Persistent Identifier | http://hdl.handle.net/10722/276504 |
ISSN | 2021 Impact Factor: 3.860 2020 SCImago Journal Rankings: 0.570 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yang, Liu | - |
dc.contributor.author | Jing, Liping | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Yu, Jian | - |
dc.date.accessioned | 2019-09-18T08:33:49Z | - |
dc.date.available | 2019-09-18T08:33:49Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Image and Vision Computing, 2016, v. 51, p. 22-35 | - |
dc.identifier.issn | 0262-8856 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276504 | - |
dc.description.abstract | © 2016 Elsevier B.V. All rights reserved. Image classification is to assign a category of an image and image annotation is to describe individual components of an image by using some annotation terms. These two learning tasks are strongly related. The main contribution of this paper is to propose a new discriminative and sparse topic model (DSTM) for image classification and annotation by combining visual, annotation and label information from a set of training images. The essential features of DSTM different from existing approaches are that (i) the label information is enforced in the generation of both visual words and annotation terms such that each generative latent topic corresponds to a category; (ii) the zero-mean Laplace distribution is employed to give a sparse representation of images in visual words and annotation terms such that relevant words and terms are associated with latent topics. Experimental results demonstrate that the proposed method provides the discrimination ability in classification and annotation, and its performance is better than the other testing methods (sLDA-ann, abc-corr-LDA, SupDocNADE, SAGE and MedSTC) for LabelMe, UIUC, NUS-WIDE and PascalVOC07 images. | - |
dc.language | eng | - |
dc.relation.ispartof | Image and Vision Computing | - |
dc.subject | Image classification | - |
dc.subject | Discriminative topic | - |
dc.subject | Graphical model | - |
dc.subject | Image annotation | - |
dc.subject | Sparsity | - |
dc.title | A discriminative and sparse topic model for image classification and annotation | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.imavis.2016.03.005 | - |
dc.identifier.scopus | eid_2-s2.0-84964253414 | - |
dc.identifier.volume | 51 | - |
dc.identifier.spage | 22 | - |
dc.identifier.epage | 35 | - |
dc.identifier.isi | WOS:000378455900003 | - |
dc.identifier.issnl | 0262-8856 | - |