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dc.contributor.authorLin, Tsong-Wuu
dc.contributor.authorHung, Chung-Shen
dc.contributor.editorJorge, Joaquim
dc.contributor.editorSkala, Václav
dc.identifier.citationWSCG '2006: Full Papers Proceedings: The 14-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2006: University of West Bohemia, Plzen, Czech Republic, January 31 – February 2, 2006, p. 209-216.en
dc.description.abstractIn this paper, we propose an image retrieval approach based on Quadrant Motif Scan (QMS). Motif scans from segmented blocks inside an image are the primary notion to extract image features. We exploit recursive quadrant segmentation in images and stratify hierarchical regions for matching comparison. Regions in the same stratum hold an identical credit, which is used for similarity metric. For the sake of matching flexibility, a dynamic adjustment scheme of credit setting is offered. In this sense, a user can arbitrarily adjust the credit parameters to pursue better retrieval results. Besides, a peak inspection technique is also added in the QMS matching metric to enhance performance. This means can helpfully refine retrieval performance with trivial computational cost. Experimental results reveal that effectiveness and efficiency of QMS are comparable to the Motif Cooccurrence Matrix (MCM) method while QMS is competent to deal with image scaling.en
dc.format8 s.cs
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG '2006: Full Papers Proceedingsen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectobsahové vyhledávánícs
dc.subjectvyhledávání obrazůcs
dc.subjectsegmentace kvadrantůcs
dc.titleQuadrant Motif Approach for Image Retrievalen
dc.typekonferenční příspěvekcs
dc.subject.translatedcontent searchen
dc.subject.translatedimage retrievalen
dc.subject.translatedquadrant segmentationen
Appears in Collections:WSCG '2006: Full Papers Proceedings

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/6650

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