Metaheuristic Optimization Algorithms for Texture Classification Using Multichannel Approaches
Summary :
This paper proposes the use of the ratio of wavelet extrema numbers taken from the horizontal and vertical counts respectively as a texture feature, which is called aspect ratio of extrema number (AREN). We formulate the classification problem upon natural and synthesized texture images as an optimization problem and develop a coevolving approach to select both scalar wavelet and multiwavelet feature spaces of greater discriminatory power. Sequential searches and genetic algorithms (GAs) are comparatively investigated. The experiments using wavelet packet decompositions with the innovative packet-tree selection scheme ascertain that the classification accuracy of coevolutionary genetic algorithms (CGAs) is acceptable enough.
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- IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.7 pp.1810-1821
- Publication Date
- 2004/07/01
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Jing-Wein WANG, "Metaheuristic Optimization Algorithms for Texture Classification Using Multichannel Approaches" in IEICE TRANSACTIONS on Fundamentals,
vol. E87-A, no. 7, pp. 1810-1821, July 2004, doi: .
Abstract: This paper proposes the use of the ratio of wavelet extrema numbers taken from the horizontal and vertical counts respectively as a texture feature, which is called aspect ratio of extrema number (AREN). We formulate the classification problem upon natural and synthesized texture images as an optimization problem and develop a coevolving approach to select both scalar wavelet and multiwavelet feature spaces of greater discriminatory power. Sequential searches and genetic algorithms (GAs) are comparatively investigated. The experiments using wavelet packet decompositions with the innovative packet-tree selection scheme ascertain that the classification accuracy of coevolutionary genetic algorithms (CGAs) is acceptable enough.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e87-a_7_1810/_p
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@ARTICLE{e87-a_7_1810,
author={Jing-Wein WANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Metaheuristic Optimization Algorithms for Texture Classification Using Multichannel Approaches},
year={2004},
volume={E87-A},
number={7},
pages={1810-1821},
abstract={This paper proposes the use of the ratio of wavelet extrema numbers taken from the horizontal and vertical counts respectively as a texture feature, which is called aspect ratio of extrema number (AREN). We formulate the classification problem upon natural and synthesized texture images as an optimization problem and develop a coevolving approach to select both scalar wavelet and multiwavelet feature spaces of greater discriminatory power. Sequential searches and genetic algorithms (GAs) are comparatively investigated. The experiments using wavelet packet decompositions with the innovative packet-tree selection scheme ascertain that the classification accuracy of coevolutionary genetic algorithms (CGAs) is acceptable enough.},
keywords={},
doi={},
ISSN={},
month={July},}
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TY - JOUR
TI - Metaheuristic Optimization Algorithms for Texture Classification Using Multichannel Approaches
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1810
EP - 1821
AU - Jing-Wein WANG
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2004
AB - This paper proposes the use of the ratio of wavelet extrema numbers taken from the horizontal and vertical counts respectively as a texture feature, which is called aspect ratio of extrema number (AREN). We formulate the classification problem upon natural and synthesized texture images as an optimization problem and develop a coevolving approach to select both scalar wavelet and multiwavelet feature spaces of greater discriminatory power. Sequential searches and genetic algorithms (GAs) are comparatively investigated. The experiments using wavelet packet decompositions with the innovative packet-tree selection scheme ascertain that the classification accuracy of coevolutionary genetic algorithms (CGAs) is acceptable enough.
ER -
English
