Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/60455
DC FieldValueLanguage
dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorWang, Z-
dc.creatorChi, Z-
dc.creatorFeng, D-
dc.creatorTsoi, AC-
dc.date.accessioned2016-12-19T08:52:00Z-
dc.date.available2016-12-19T08:52:00Z-
dc.identifier.issn0219-4678-
dc.identifier.urihttp://hdl.handle.net/10397/60455-
dc.language.isoenen_US
dc.publisherWorld Scientificen_US
dc.subjectImage representationen_US
dc.subjectContent-based image retrievalen_US
dc.subjectRelevance feedbacken_US
dc.subjectAdaptive processing of data structuresen_US
dc.subjectBack-propagation through structureen_US
dc.subjectNeural networksen_US
dc.titleContent-based image retrieval with relevance feedback using adaptive processing of tree-structure image representationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage119-
dc.identifier.epage143-
dc.identifier.volume3-
dc.identifier.issue1-
dc.identifier.doi10.1142/S0219467803000944-
dcterms.abstractContent-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.-
dcterms.bibliographicCitationInternational journal of image and graphics, 2003, v. 3, no. 1, p. 119-143-
dcterms.isPartOfInternational journal of image and graphics-
dcterms.issued2003-
dc.identifier.eissn1793-6756-
dc.identifier.rosgroupidr12895-
dc.description.ros2002-2003 > Academic research: refereed > Publication in refereed journal-
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