Abo Bibliothek: Guest
Digitales Portal Digitale Bibliothek eBooks Zeitschriften Referenzen und Berichte Forschungssammlungen
Journal of Flow Visualization and Image Processing
SJR: 0.161 SNIP: 0.312 CiteScore™: 0.1

ISSN Druckformat: 1065-3090
ISSN Online: 1940-4336

Journal of Flow Visualization and Image Processing

DOI: 10.1615/JFlowVisImageProc.2012005150
pages 97-111

BLIND FEATURE SELECTION AND EXTRACTION IN A 3D IMAGE CUBE

Muhammad Ahmad
Department of Computer Engineering, Kyung Hee University, South Korea
Syungyoung Lee
Department of Computer Engineering, Kyung Hee University, South Korea
Ihsan Ul Haq
Department of Electronics Engineering, International Islamic University (IIU), Pakistan
Qaisar Mushtaq
Department of Computer Science, National Textile University (NTU), Faisalabad, Pakistan

ABSTRAKT

As the number of spectral hands of high spectral resolution increases, the ability to detect more detailed classes should also increase, and the classification accuracy should increase as well. As the number of spectral bands becomes large, the limitation on performance imposed by the limited number of observed samples can become severe. A number of techniques for specific feature selection and extraction have been developed to reduce the dimensionality and target detection without the loss of class reparability. This suggests the need for reducing the dimensionality via a preprocessing method that takes into consideration the high dimensional feature space properties. Such reduction should enable the estimation of the feature extraction parameters to be more accurate bu using the technique referred to as the maximum conditional log likelihood estimation. This technique is able to bypass many of the problems of the limitation of small/large numbers of observed samples by making the computations in a lower dimensional space, and optimizing the function called the expectation and maximization. This method leads also to a high dimensional version of the feature selection and extraction algorithm, which requires significantly less computation than the normal procedure. A set of tests with real data evaluates the performance and illustrates the effectiveness of the proposed method. The entire work is done by using MATLAB.


Articles with similar content:

On the Structural and Parametric Identification under the Limited Uncertainty and Approximating Models of Multidimensional Systems
Journal of Automation and Information Sciences, Vol.43, 2011, issue 6
Alexey V. Gummel, Vyacheslav F. Gubarev, Artem A. Kryshtal, Vladislav Yu. Oles
EFFECTIVE SIGNAL DETECTION FOR THE SPATIAL MULTIPLEXING MIMO SYSTEMS
Telecommunications and Radio Engineering, Vol.77, 2018, issue 13
V. B. Kreyndelin , А. P. Shumov, М. G. Bakulin, V. V. Vityazev
APPLICATION OF THE MATRIX PENCIL METHOD FOR RADAR MEASUREMENTS OF ELEVATION ANGLES OF LOW-ALTITUDE TARGETS OVER A DISTURBED SEA
Telecommunications and Radio Engineering, Vol.77, 2018, issue 9
Yu. A. Pedenko
A GENERAL FRAMEWORK FOR ENHANCING SPARSITY OF GENERALIZED POLYNOMIAL CHAOS EXPANSIONS
International Journal for Uncertainty Quantification, Vol.9, 2019, issue 3
Xiu Yang, Xiaoliang Wan, Huan Lei, Lin Lin
LOW-COST MULTI-DIMENSIONAL GAUSSIAN PROCESS WITH APPLICATION TO UNCERTAINTY QUANTIFICATION
International Journal for Uncertainty Quantification, Vol.5, 2015, issue 4
Guang Lin, Bledar A. Konomi