School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Comput Intell Neurosci. 2016;2016:8179670. doi: 10.1155/2016/8179670. Epub 2016 Dec 19.
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.
纹理图像分类是机器视觉和图像分析中许多应用的重要课题。通过使用“调谐”掩模从原始纹理图像中提取纹理特征是最简单、最有效的方法之一。然而，基于爬山的训练方法不能一次获得满意的掩模；另一方面，一些常用的进化算法，如遗传算法（GA）和粒子群优化（PSO），很容易陷入局部最优。本文详细介绍了一种用于纹理图像分类的新方法，以识别居民区为例。在提出的方法中，将“调谐”掩模视为约束优化问题，并通过最大化纹理能量来获取最佳“调谐”掩模，这是通过新提出的引力搜索算法（GSA）实现的。通过 GSA 的收敛来获得最佳“调谐”掩模。该方法分别在一些公共纹理和遥感图像上进行了测试。然后将结果与 GA、PSO、蜜蜂交配优化（HBMO）和人工免疫算法（AIA）进行比较。此外，还利用 Gabor 小波提取的特征进行了进一步的比较。实验结果表明，与本文中涉及的其他方法相比，该方法具有鲁棒性和适应性，在适应值和分类精度方面表现出更好的性能。
