Natural and Remote Sensing Image Segmentation Using Memetic Computing
Natural and Remote Sensing Image Segmentation Using Memetic Computing
复制标题
使用模因计算进行自然和遥感图像分割
DOI：
10.1109/mci.2010.936307
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发表时间：
2010-05
影响因子：
9
通讯作者：
Wu, Qiaodi
中科院分区：
文献类型：
--
作者：
Jiao, Licheng;Gong, Maoguo;Wang, Shuang;Hou, Biao;Zheng, Zhi;Wu, Qiaodi
为了解决图像分割问题，即为图像中的每个像素分配一个标签，使具有相同标签的像素更有效地共享某些视觉特征，提出了一种基于模因算法的图像分割方法。分水岭分割首先将原始图像分割成不重叠的小区域，然后再进行MISA分割。MISA采用一种直观的表示方法，在特征空间的类间方差准则下寻找流域区域的最优组合。在实现了基于聚类的交叉和变异之后，个体学习程序根据当前聚类中的外心区域与聚类中心在特征空间上的距离，将这些区域移动到它们应该属于的区域。为了评估新算法，实验中使用了6幅纹理图像、3幅遥感图像和3幅自然图像。实验结果表明，MISA在大多数测试问题的分割方面优于遗传算法、模糊c均值算法和k均值算法，并且与两种最先进的图像分割算法（包括高效的基于图的算法和基于谱聚类集成的算法）相比，MISA是一种有效的方法。
In order to solve the image segmentation problem which assigns a label to every pixel in an image such that pixels with the same label share certain visual characteristics more effectively, a novel approach based on memetic algorithm (MISA) is proposed. Watershed segmentation is applied to segment original images into non-overlap small regions before performing the portioning process by MISA. MISA adopts a straightforward representation method to find the optimal combination of watershed regions under the criteria of interclass variance in feature space. After implementing cluster-based crossover and mutation, an individual learning procedure moves exocentric regions in current cluster to the one they should belong to according to the distance between these regions and cluster centers in feature space. In order to evaluate the new algorithm, six texture images, three remote sensing images and three natural images are employed in experiments. The experimental results show that MISA outperforms its genetic version, the Fuzzy c-means algorithm, and K-means algorithm in partitioning most of the test problems, and is an effective approach when compared with two state-ofthe-art image segmentation algorithms including an efficient graph-based algorithm and a spectral clustering ensemble-based algorithm.
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影响因子：
14.3
作者：
Handl, Julia;Knowles, Joshua
通讯作者：
Knowles, Joshua
DOI：
10.1109/cec.2004.1330985
发表时间：
2004-06
期刊：
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)
影响因子：
--
作者：
Elsa Fernández;M. Graña;J. Ruíz-Cabello
通讯作者：
Elsa Fernández;M. Graña;J. Ruíz-Cabello
DOI：
10.1016/s0262-8856(98)00095-x
发表时间：
1999-03
期刊：
Image Vis. Comput.
影响因子：
--
作者：
P. Andrey
通讯作者：
P. Andrey
影响因子：
14.3
作者：
Kazarlis, SA;Papadakis, SE;Petridis, V
通讯作者：
Petridis, V
影响因子：
8
作者：
Maulik, U;Bandyopadhyay, S
通讯作者：
Bandyopadhyay, S