Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICASSP.2009.4959711
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dc.titleDirected Markov stationary features for visual classification
dc.contributor.authorNi, B.
dc.contributor.authorYan, S.
dc.contributor.authorKassim, A.
dc.date.accessioned2014-06-19T03:06:41Z
dc.date.available2014-06-19T03:06:41Z
dc.date.issued2009
dc.identifier.citationNi, B., Yan, S., Kassim, A. (2009). Directed Markov stationary features for visual classification. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings : 825-828. ScholarBank@NUS Repository. https://doi.org/10.1109/ICASSP.2009.4959711
dc.identifier.isbn9781424423545
dc.identifier.issn15206149
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/69966
dc.description.abstractWe investigate how to effectively incorporate spatial structure information into histogram features for boosting visual classification performance motivated by recently proposed Markov Stationary Features (MSF). First, we show that due to the symmetric property of the image occurrence modeling procedure, the stationary distribution derived from the normalized co-occurrence matrix has a trivial informative solution which only approximates the original histogram representation, i.e., does not encode proper spatial structure information. To eliminate this ambiguity, we propose in this work the so called Directed Markov Stationary Features (DMSF) to encode spatial information into histogram features, and the asymmetric essence of the co-occurrence matrices in DMSF avoids the trivial informative solutions in MSF. Extensive experiments on face recognition show the significant performance improvement brought by our proposed DMSF. ©2009 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICASSP.2009.4959711
dc.sourceScopus
dc.subjectDirected Markov stationary features
dc.subjectMarkov stationary features
dc.subjectVisual classification
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/ICASSP.2009.4959711
dc.description.sourcetitleICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
dc.description.page825-828
dc.description.codenIPROD
dc.identifier.isiut000268919200207
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