Dimensionality reduction and visualisation of hyperspectral ink data using t-SNE.
Ink analysis is an important tool in forensic science and document analysis. Hyperspectral imaging (HSI) captures large number of narrowband images across the electromagnetic spectrum. HSI is one of the non-invasive tools used in forensic document analysis, especially for ink analysis. The substanti...
| Published in: | Forensic Science International Vol. 311 |
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| Main Authors: | , |
| Format: | Journal Article |
| Published: |
Elsevier B.V.
Jun2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143310010&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143310010 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03790738 3L0 jtl: Forensic Science International issn: 03790738 maglogo: N pubinfo: dt: Jun2020 vid: 311 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 143310010 143310010 NLM32251968 10.1016/j.forsciint.2020.110194 NLM32251968 143310010 ppct: 1 formats: tig: atl: Dimensionality reduction and visualisation of hyperspectral ink data using t-SNE. aug: au: Melit Devassy, Binu George, Sony affil: Department of Computer Science, Norwegian University of Science and Technology, Gjøvik, Norway sug: ab: Ink analysis is an important tool in forensic science and document analysis. Hyperspectral imaging (HSI) captures large number of narrowband images across the electromagnetic spectrum. HSI is one of the non-invasive tools used in forensic document analysis, especially for ink analysis. The substantial information from multiple bands in HSI images empowers us to make non-destructive diagnosis and identification of forensic evidence in questioned documents. The presence of numerous band information in HSI data makes processing and storing becomes a computationally challenging task. Therefore, dimensionality reduction and visualization play a vital role in HSI data processing to achieve efficient processing and effortless understanding of the data. In this paper, an advanced approach known as t-Distributed Stochastic Neighbor embedding (t-SNE) algorithm is introduced into the ink analysis problem. t-SNE extracts the non-linear similarity features between spectra to scale them into a lower dimension. This capability of the t-SNE algorithm for ink spectral data is verified visually and quantitatively, the two-dimensional data generated by the t-SNE showed a better visualization and a greater improvement in clustering quality in comparison with Principal Component Analysis (PCA). pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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