Image feature extraction using compressive sensing

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage184en_US
dc.citation.spage177en_US
dc.citation.volumeNumber233en_US
dc.contributor.authorEleyan, A.en_US
dc.contributor.authorKöse, Kıvançen_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialBydgoszcz, Polanden_US
dc.date.accessioned2016-02-08T11:41:17Zen_US
dc.date.available2016-02-08T11:41:17Zen_US
dc.date.issued2014en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 11-13 September 2013en_US
dc.descriptionConference Name: 5th International Conference on Image Processing and Communications, IP&C 2013en_US
dc.description.abstractIn this paper a new approach for image feature extraction is presented. We used the Compressive Sensing (CS) concept to generate the measurement matrix. The new measurement matrix is different from the measurement matrices in literature as it was constructed using both zero mean and nonzero mean rows. The image is simply projected into a new space using the measurement matrix to obtain the feature vector. Another proposed measurement matrix is a random matrix constructed from binary entries. Face recognition problem was used as an example for testing the feature extraction capability of the proposed matrices. Experiments were carried out using two well-known face databases, namely, ORL and FERET databases. System performance is very promising and comparable with the classical baseline feature extraction algorithms.en_US
dc.identifier.doi10.1007/978-3-319-01622-1_21en_US
dc.identifier.issn2194-5357en_US
dc.identifier.urihttp://hdl.handle.net/11693/26986en_US
dc.language.isoEnglishen_US
dc.publisherSpringeren_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-319-01622-1_21en_US
dc.source.titleImage Processing and Communications Challenges 5en_US
dc.subjectMeasurement matrixen_US
dc.subjectCompressed sensingen_US
dc.subjectFace imageen_US
dc.subjectFace recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectSignal reconstructionen_US
dc.subjectVector spacesen_US
dc.titleImage feature extraction using compressive sensingen_US
dc.typeConference Paperen_US
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