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DC poleHodnotaJazyk
dc.contributor.authorTewari, Aditya
dc.contributor.authorTaetz, Bertram
dc.contributor.authorGrandidier, Frédéric
dc.contributor.editorSkala, Václav
dc.date.accessioned2016-01-07T07:16:37Z
dc.date.available2016-01-07T07:16:37Z
dc.date.issued2015
dc.identifier.citationJournal of WSCG. 2015, vol. 23, no. 1, p. 35-43.en
dc.identifier.issn1213–6972 (hardcopy)
dc.identifier.issn1213–6980 (CD-ROM)
dc.identifier.issn1213–6964 (online)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2015/!_2015_Journal_WSCG-No-1.pdf
dc.identifier.urihttp://hdl.handle.net/11025/17142
dc.format9 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesJournal of WSCGen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectSFAcs
dc.subjectidentifikace držení rukycs
dc.subjectextrakce znalostícs
dc.subjectučení vlastnostícs
dc.titleUsing mutual independence of slow features for improved information extraction and better hand-pose classificationen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedWe propose a Slow Feature Analysis (SFA) based classification of hand-poses and demonstrate that the property of mutual independence of the slow feature functions improves the classification performance. SFA extracts functions that describe trends in a time series data and is capable of isolating noise from information while conserving high-frequency components of the data which are consistently present over time or in the set of data points. SFA is a useful knowledge extraction method that can be modified to identify functions which are well suited for distinguishing classes. We show that by using the orthogonality property of SFA our information about classes can be increased. This is demonstrated by classification results on the well known MNIST dataset for hand written digit detection. Furthermore, we use a hand-pose dataset with five possible classes to show the performance of SFA. It consistently achieves a detection rate of over 96% for each class. We compare the classification results on shape descriptive physical features, on the Principal Component Analysis (PCA) and the non-linear dimensionality reduction (NLDR) for manifold learning. We show that a simple variance based decision algorithm for SFA gives higher recognition rates than K-Nearest Neighbour (KNN), on physical features, PCA and non-linear low dimensional representation. Finally, we examine Convolutional Neural Networks (CNN) in relation with SFA.en
dc.subject.translatedSFAen
dc.subject.translatedhand-pose identificationen
dc.subject.translatedknowledge extractionen
dc.subject.translatedfeature learningen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 23, Number 1 (2015)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 23 (2015)
  6. Volume 23, Number 1 (2015)