State Grid Hunan Electric Power Corporation Economy Institute, Changsha 410003, China.
State Grid Hunan Electric Power Company Limited, Changsha 410003, China.
Comput Intell Neurosci. 2018 Dec 9;2018:7690435. doi: 10.1155/2018/7690435. eCollection 2018.
Texture feature extraction is a key topic in many applications of image analysis; a lot of techniques have been proposed to measure the characteristics of this field. Among them, texture energy extracted with a mask is a rotation and scale invariant texture descriptor. However, the tuning process is computationally intensive and easily trap into the local optimum. In the proposed approach, a "Tuned" mask is utilized to extract water and nonwater texture; the optimal "Tuned" mask is acquired by maximizing the texture energy value via a newly proposed cuckoo search (CS) algorithm. Experimental results on samples and images show that the proposed method is suitable for texture feature extraction, the recognition accuracy is higher than the genetic algorithm (GA), particle swarm optimization (PSO) and the gravitational search algorithm (GSA) optimized "Tuned" mask scheme, and the water area could be well recognized from the original image. Experimental results show that the proposed method could exhibit better performance than other methods involved in the paper in terms of optimization ability and recognition result.
纹理特征提取是图像分析中许多应用的关键主题；已经提出了许多技术来测量该领域的特征。其中，用掩模提取的纹理能量是一种旋转和尺度不变的纹理描述符。然而，调谐过程计算量很大，并且容易陷入局部最优。在提出的方法中，利用“调谐”掩模来提取水和非水纹理；通过新提出的布谷鸟搜索（CS）算法最大化纹理能量值来获得最佳的“调谐”掩模。对样本和图像的实验结果表明，该方法适用于纹理特征提取，识别精度高于遗传算法（GA）、粒子群优化（PSO）和引力搜索算法（GSA）优化的“调谐”掩模方案，并且可以从原始图像中很好地识别水区域。实验结果表明，与本文涉及的其他方法相比，该方法在优化能力和识别结果方面表现出更好的性能。
