摘要
针对传统阈值法在图像分割时仅考虑像素的灰度信息,对噪声敏感且不易确定全局最优阈值的缺点,提出了基于蚁群智能和支持向量机的图像分割方法。该方法利用蚁群优化算法对图像的二维阈值空间进行全局搜索,并将搜索到的最优点灰度—区域灰度均值对作为阈值来区分目标和背景,然后对支持向量机进行训练和测试,最后用训练好的支持向量机分割图像。实验结果表明,该方法抗噪能力强,分割准确,是一种实用有效的图像分割方法。
A method for image segmentation based on ant colony intelligence and SVM is proposed. It aims at the disadvantages of the traditional threshold methods, such as only considering the gray information of pixels; sensitive to noise; difficult to determine the global optimal threshold and so on. First, ant colony optimization algorithm is used to search the 2-D threshold space of the image. Second, the optimal pixel-scale gray is used as the threshold to distinguish the target from the background. Third, SVM is trained and tested. Finally, the image is segmented by trained SVM. The experiment simulation results show that this method is an applied and effective method for image segmentation with high ability of resisting noise and exact effect of segmentation.
出处
《计算机工程与设计》
CSCD
北大核心
2009年第2期408-410,共3页
Computer Engineering and Design
基金
湖南省自然科学基金项目(06JJ50109)
关键词
图像分割
蚁群智能
优化算法
支持向量机
二维阈值
image segmentation
ant colony intelligence
optimization algorithm
support vector machine
two-dimensional threshold
