Metaheuristic Optimization Algorithms for Texture Classification Using Multichannel Approaches

Jing-Wein WANG  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E87-A   No.7   pp.1810-1821
Publication Date: 2004/07/01
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Image
aspect ratio of extrema number (AREN),  texture classification,  genetic algorithms (GAs),  coevolutionary genetic algorithms (CGAs),  packet-tree selection scheme,  

Full Text: PDF>>
Buy this Article

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.