A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm
Joint Authors
Wang, Mingwei
Lai, Xudong
Wan, Youchuan
Ye, Zhiwei
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-19
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
Texture image classification is an important topic in many applications in machine vision and image analysis.
Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods.
However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum.
A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper.
In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA).
The optimal “Tuned” mask is achieved through the convergence of GSA.
The proposed approach has been, respectively, tested on some public texture and remote sensing images.
The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA).
Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison.
Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.
American Psychological Association (APA)
Wan, Youchuan& Wang, Mingwei& Ye, Zhiwei& Lai, Xudong. 2016. A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1099782
Modern Language Association (MLA)
Wan, Youchuan…[et al.]. A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-16.
https://search.emarefa.net/detail/BIM-1099782
American Medical Association (AMA)
Wan, Youchuan& Wang, Mingwei& Ye, Zhiwei& Lai, Xudong. A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1099782
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099782