Optimal sign selection for discriminant analysis of textures
| Autor: | Andry V. Kovalenko, Vitalij N. Kurashov, Alexandr V. Kisil, Alexandr G. Chumakov |
|---|---|
| Rok vydání: | 1995 |
| Předmět: |
education.field_of_study
Pixel business.industry Population ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition White noise Linear discriminant analysis Image (mathematics) Computer Science::Computer Vision and Pattern Recognition Feature (machine learning) Artificial intelligence education business Selection (genetic algorithm) Sign (mathematics) Mathematics |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.217425 |
| Popis: | In this paper, we investigate the problem of optimal (feature) selection for texture recognition for the case, when statistical properties of the image general population are satisfactorally represented by the a prior classified training set of small size (i.e. the number of images in the training set is much smaller then the number of pixels on the image). We examine criteria, defined by the trace norm of the certain self-conjugate operator, constructed in the special manner from the elements of the training set. Karhunen-Loeve expansion, Hoteling criteria, and some of their modifications are considered for recognition of computer generated regular textures, distorted with white noise. Comparative analysis of criteria efficiency is presented for several possible kinds of classification of the training set. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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