title: A "Tuned" Mask Learnt Approach Based on Gravitational Search Algorithm.
abstract: 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.
authors.affiliation: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
authors.forename: Youchuan
authors.initials: Y
authors.lastname: Wan
authors.affiliation: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
authors.forename: Mingwei
authors.identifier: 0000-0002-0799-3311
authors.initials: M
authors.lastname: Wang
authors.affiliation: School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Ye
authors.affiliation: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
authors.forename: Xudong
authors.identifier: 0000-0003-4611-820X
authors.initials: X
authors.lastname: Lai
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D006112:Gravitation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D010363:Pattern Recognition, Automated