Methods of two-dimensional projection of digital images into eigen-subspaces: peculiarities of implementation and application
| Autor: | Georgy Kukharev, N. L. Shchegoleva |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
face image (FI) sets and numeric matrices
Computer science canonical correlation analysis (CCA) partial least squares (PLS) principal components analysis (PCA) Digital image 2DPLS/2DKLT linear discriminant analysis (LDA) lcsh:Information theory lcsh:QC350-467 Computer vision Electrical and Electronic Engineering 2DPCA/2DKLT Projection (set theory) 2DCCA/2DKLT an eigen-basis and eigensubspaces business.industry Deep NNю Linear subspace lcsh:Q350-390 Atomic and Molecular Physics and Optics Computer Science Applications Karhunen-Loeve transformation (KLT) Artificial intelligence business CNN Deep NN lcsh:Optics. Light |
| Zdroj: | Компьютерная оптика, Vol 42, Iss 4, Pp 637-656 (2018) |
| ISSN: | 2412-6179 0134-2452 |
| Popis: | The history of development of algorithms for projection of digital images into their eigensubspaces using linear methods based on PCA (principal component analysis), LDA (linear discriminant analysis), PLS (partial least squares), and CCA (canonical correlation analysis) is considered. We show that the emergence of new application areas has changed the requirements for the methods, with major changes involving (i) the use of PCA, LDA, PLS and CCA methods for both small and extremely large face image (FI) samples in the initial sets; (ii) a criterion for determining the eigen-basis, which also should provide the solution of a particular problem (the minimum error of face image approximation, etc.); (iii) the applicability of the methods under consideration to the processing of two or more image sets from different sensors or several sets of any number of matrices; and (iv) the possibility of realizing two-dimensional projections of face images (or other numerical matrices) directly into the layers of convolutional neural networks (NN) and/or integrating their functions into the NN as separate blocks. Estimates of the computational complexity and examples of solving image processing problems are also given. |
| Databáze: | OpenAIRE |
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