Optimal sign selection for discriminant analysis of textures

Vitalij N. Kurashov; Andry V. Kovalenko; Alexandr G. Chumakov; Alexandr V. Kisil

doi:10.1117/12.217425

22 August 1995 Optimal sign selection for discriminant analysis of textures

Vitalij N. Kurashov, Andry V. Kovalenko, Alexandr G. Chumakov, Alexandr V. Kisil

Proceedings Volume 2564, Applications of Digital Image Processing XVIII; (1995) https://doi.org/10.1117/12.217425
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

Abstract

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.

Citation Download Citation

Vitalij N. Kurashov, Andry V. Kovalenko, Alexandr G. Chumakov, and Alexandr V. Kisil "Optimal sign selection for discriminant analysis of textures", Proc. SPIE 2564, Applications of Digital Image Processing XVIII, (22 August 1995); https://doi.org/10.1117/12.217425

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