Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorShahabi Lotfabadi, Maryam
dc.contributor.authorFairuz Shiratuddin, Mohd
dc.contributor.authorWai Wong, Kok
dc.contributor.editorSkala, Václav
dc.date.accessioned2017-10-10T08:21:46Z
dc.date.available2017-10-10T08:21:46Z
dc.date.issued2014
dc.identifier.citationWSCG 2014: communication papers proceedings: 22nd International Conference in Central Europeon Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 95-102.en
dc.identifier.isbn978-80-86943-71-8
dc.identifier.uriwscg.zcu.cz/WSCG2014/!!_2014-WSCG-Communication.pdf
dc.identifier.urihttp://hdl.handle.net/11025/26383
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2014: communication papers proceedingsen
dc.rights@ Václav Skala - UNION Agencycs
dc.subjectfuzzy hrubá množinacs
dc.subjectobsah založený na systému načítání obrazucs
dc.subjecthlukcs
dc.titleEvaluation of fuzzy rough set feature selection for content based image retrieval system with noisy imagesen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIn this paper Fuzzy Rough Set is used for feature selection in the Content Based Image Retrieval system. Noisy query images are fed to this Content Based Image Retrieval system and the results are compared with four other feature selection methods. The four other feature selection methods are Genetic Algorithm, Information Gain, OneR and Principle Component Analysis. The main objective of this paper is to evaluate the rules which are extracted from fuzzy rough set and determine whether these rules which are used for training the Support Vector Machine can deal with noisy query images as well as the original queried images. To evaluate the Fuzzy Rough set feature selection, we use 10 sematic group images from COREL database which we have purposely placed some defect by adding Gaussian, Poisson and Salt and Pepper noises of different magnitudes. As a result, the proposed method performed better in term of accuracies in most of the different types of noise when compared to the other four feature selection methods.en
dc.subject.translatedfuzzy rough seten
dc.subject.translatedcontent based image retrieval systemen
dc.subject.translatednoiseen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2014: Communication Papers Proceedings

Soubory připojené k záznamu:
Soubor Popis VelikostFormát 
Lotfabadi.pdfPlný text974,73 kBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/26383

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Sborníky ZČU / UWB Proceedings
  4. WSCG: Communication Papers
  5. WSCG 2014: Communication Papers Proceedings