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dc.contributor.authorMelit Devassy, Binu
dc.contributor.authorGeorge, Sony
dc.contributor.authorNussbaum, Peter
dc.date.accessioned2020-05-18T12:54:00Z
dc.date.available2020-05-18T12:54:00Z
dc.date.created2020-05-06T16:33:30Z
dc.date.issued2020
dc.identifier.issn2313-433X
dc.identifier.urihttps://hdl.handle.net/11250/2654822
dc.description.abstractFor a suspected forgery that involves the falsification of a document or its contents, the investigator will primarily analyze the document’s paper and ink in order to establish the authenticity of the subject under investigation. As a non-destructive and contactless technique, Hyperspectral Imaging (HSI) is gaining popularity in the field of forensic document analysis. HSI returns more information compared to conventional three channel imaging systems due to the vast number of narrowband images recorded across the electromagnetic spectrum. As a result, HSI can provide better classification results. In this publication, we present results of an approach known as the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, which we have applied to HSI paper data analysis. Even though t-SNE has been widely accepted as a method for dimensionality reduction and visualization of high dimensional data, its usefulness has not yet been evaluated for the classification of paper data. In this research, we present a hyperspectral dataset of paper samples, and evaluate the clustering quality of the proposed method both visually and quantitatively. The t-SNE algorithm shows exceptional discrimination power when compared to traditional PCA with k-means clustering, in both visual and quantitative evaluations.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleUnsupervised Clustering of Hyperspectral Paper Data Using t-SNEen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.volume6en_US
dc.source.journalJournal of Imagingen_US
dc.source.issue5en_US
dc.identifier.doihttps://doi.org/10.3390/jimaging6050029
dc.identifier.cristin1809706
dc.description.localcode© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.fulltextoriginal
cristin.qualitycode1


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