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dc.contributor.authorEleyan, A.en_US
dc.contributor.authorKose, K.en_US
dc.contributor.authorCetin, A.E.en_US
dc.date.accessioned2016-02-08T11:41:17Z
dc.date.available2016-02-08T11:41:17Z
dc.date.issued2014en_US
dc.identifier.issn21945357
dc.identifier.urihttp://hdl.handle.net/11693/26986
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. © Springer International Publishing Switzerland 2014.en_US
dc.language.isoEnglishen_US
dc.source.titleAdvances in Intelligent Systems and Computingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-319-01622-1_21en_US
dc.subjectAlgorithmsen_US
dc.subjectCompressed sensingen_US
dc.subjectExtractionen_US
dc.subjectFace recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectSignal reconstructionen_US
dc.subjectVector spacesen_US
dc.subjectCompressive sensingen_US
dc.subjectExtraction capabilityen_US
dc.subjectFeature extraction algorithmsen_US
dc.subjectFeature vectorsen_US
dc.subjectFERET databaseen_US
dc.subjectImage feature extractionsen_US
dc.subjectMeasurement matrixen_US
dc.subjectNew approachesen_US
dc.subjectImage processingen_US
dc.titleImage feature extraction using compressive sensingen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineering
dc.citation.spage177en_US
dc.citation.epage184en_US
dc.citation.volumeNumber233en_US
dc.identifier.doi10.1007/978-3-319-01622-1_21en_US
dc.publisherSpringer Verlagen_US


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