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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >An approach for learning the optimal “tuned” masks based on differential evolution algorithm
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An approach for learning the optimal “tuned” masks based on differential evolution algorithm

机译：一种基于差分进化算法的最优“调优”掩模学习方法

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摘要

Texture image classification is a significant topic in many applications of machine vision and image analysis. The texture feature extracted from the original image by using the “Tuned” mask is one of the simplest and most effective methods. However, the primary gradient based training method almost always falls into the local optimum which might be improved through some commonly used evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO). Unfortunately, these algorithms will easily trap into the local optimum as well. For the sake of learning “Tuned” mask with the better performance, this paper propose to employ differential evolution algorithm to generate the optimal “Tuned” mask. Experiments on some texture images from the Brodatz album show that the “Tuned” mask training method proposed in this paper is very effective for classifying texture images and outperforms the “Tuned” mask training method based on genetic algorithm and particle swarm optimization algorithm.
机译：纹理图像分类是机器视觉和图像分析的许多应用中的重要课题。使用“ Tuned”蒙版从原始图像中提取的纹理特征是最简单，最有效的方法之一。然而，基于梯度的主要训练方法几乎总是属于局部最优，这可以通过一些常用的进化算法（例如遗传算法（GA）和粒子群优化（PSO））加以改进。不幸的是，这些算法也很容易陷入局部最优。为了学习具有更好性能的“ Tuned”掩模，本文提出采用差分进化算法生成最优的“ Tuned”掩模。对Brodatz专辑的一些纹理图像的实验表明，本文提出的“调”掩模训练方法对纹理图像的分类非常有效，优于基于遗传算法和粒子群优化算法的“调”掩模训练方法。

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