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Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE
- Source :
- Journal of Imaging, Journal of Imaging, Vol 6, Iss 29, p 29 (2020), Volume 6, Issue 5
- Publication Year :
- 2020
- Publisher :
- MDPI, 2020.
-
Abstract
- For a suspected forgery that involves the falsification of a document or its contents, the investigator will primarily analyze the document&rsquo<br />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.
- Subjects :
- Clustering high-dimensional data
forensic paper analysis
Channel (digital image)
Computer science
0211 other engineering and technologies
02 engineering and technology
lcsh:Computer applications to medicine. Medical informatics
01 natural sciences
Article
Field (computer science)
lcsh:QA75.5-76.95
hyperspectral unsupervised clustering
Radiology, Nuclear Medicine and imaging
lcsh:Photography
Electrical and Electronic Engineering
Cluster analysis
021101 geological & geomatics engineering
hyperspectral dimensionality reduction
business.industry
Dimensionality reduction
010401 analytical chemistry
Hyperspectral imaging
Pattern recognition
lcsh:TR1-1050
Computer Graphics and Computer-Aided Design
t-SNE
0104 chemical sciences
Visualization
forensic document analysis
Embedding
lcsh:R858-859.7
Computer Vision and Pattern Recognition
Artificial intelligence
lcsh:Electronic computers. Computer science
business
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Journal of Imaging, Journal of Imaging, Vol 6, Iss 29, p 29 (2020), Volume 6, Issue 5
- Accession number :
- edsair.doi.dedup.....247579f2b7169924c619ade330c24850