Full metadata record
DC FieldValueLanguage
dc.contributor.authorSchreck, Tobias
dc.contributor.authorSchneidewind, Jörn
dc.contributor.authorKeim, Daniel A.
dc.contributor.editorCunningham, Steve
dc.contributor.editorSkala, Václav
dc.date.accessioned2014-04-25T09:29:29Z-
dc.date.available2014-04-25T09:29:29Z-
dc.date.issued2008
dc.identifier.citationWSCG '2008: Communication Papers: The 16-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech Republic, February 4 - 7, 2008, p. 223-230.en
dc.identifier.isbn978-80-86943-16-9
dc.identifier.urihttp://wscg.zcu.cz/wscg2008/Papers_2008/short/!_WSCG2008_Short_final.zip
dc.identifier.urihttp://hdl.handle.net/11025/11126
dc.description.abstractMethods for management and analysis of non-standard data often rely on the so-called feature vector approach. The technique describes complex data instances by vectors of characteristic numeric values which allow to index the data and to calculate similarity scores between the data elements. Thereby, feature vectors often are a key ingredient to intelligent data analysis algorithms including instances of clustering, classification, and similarity search algorithms. However, identification of appropriate feature vectors for a given database of a given data type is a challenging task. Determining good feature vector extractors usually involves benchmarks relying on supervised information, which makes it an expensive and data dependent process. In this paper, we address the feature selection problem by a novel approach based on analysis of certain feature space images. We develop two image-based analysis techniques for the automatic discrimination power analysis of feature spaces. We evaluate the techniques on a comprehensive feature selection benchmark, demonstrating the effectiveness of our analysis and its potential toward automatically addressing the feature selection problem.en
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoencs
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG '2008: Communication Papersen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectvizuální analytikacs
dc.subjectpříznakové vektorycs
dc.subjectautomatický výběr znakůcs
dc.subjectsamoorganizující se mapycs
dc.titleAn Image-Based Approach to Visual Feature Space Analysisen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.subject.translatedvisual analyticsen
dc.subject.translatedfeature vectorsen
dc.subject.translatedautomatic feature selectionen
dc.subject.translatedself-organizing mapsen
dc.type.statusPeer-revieweden
dc.type.driverinfo:eu-repo/semantics/conferenceObjecten
dc.type.driverinfo:eu-repo/semantics/publishedVersionen
Appears in Collections:WSCG '2008: Communication Papers

Files in This Item:
File Description SizeFormat 
Schreck.pdfPlný text3,01 MBAdobe PDFView/Open


Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/11126

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.