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Content based image retrieval using unclean positive examples

Zhang, Jun and Ye, Lei 2009, Content based image retrieval using unclean positive examples, IEEE transactions on image processing, vol. 18, no. 10, pp. 2370-2375, doi: 10.1109/TIP.2009.2026669.

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Title Content based image retrieval using unclean positive examples
Author(s) Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Ye, Lei
Journal name IEEE transactions on image processing
Volume number 18
Issue number 10
Start page 2370
End page 2375
Total pages 6
Publisher I E E E
Place of publication Piscataway, N. J.
Publication date 2009-10
ISSN 1057-7149
1941-0042
Keyword(s) Classifier combination
content-based image retrieval (CBIR)
feature aggregation
noise tolerant
support vector machine (SVM)
Summary Conventional content-based image retrieval (CBIR) schemes employing relevance feedback may suffer from some problems in the practical applications. First, most ordinary users would like to complete their search in a single interaction especially on the web. Second, it is time consuming and difficult to label a lot of negative examples with sufficient variety. Third, ordinary users may introduce some noisy examples into the query. This correspondence explores solutions to a new issue that image retrieval using unclean positive examples. In the proposed scheme, multiple feature distances are combined to obtain image similarity using classification technology. To handle the noisy positive examples, a new two-step strategy is proposed by incorporating the methods of data cleaning and noise tolerant classifier. The extensive experiments carried out on two different real image collections validate the effectiveness of the proposed scheme.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Language eng
DOI 10.1109/TIP.2009.2026669
Field of Research 080704 Information Retrieval and Web Search
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890301 Electronic Information Storage and Retrieval Services
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30039533

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.