基于粗糙集理论的图像分割智能决策方法

An Intelligent Decision Method of Image Segmentation Based on Rough Set Theory

尽管如今已有多种图像分割算法,但是没有任何一种分割方法能够适用于所有的图像。为了使图像跟踪系统能根据图像特征自适应选取分割算法,给出了一种基于粗糙集理论的图像分割智能决策方法。该方法首先选取若干具代表性的分割算法构成算法库,并用它们对各种样本图像进行分割;然后利用从样本图像中提取出来的各种数值特征,并根据图像分割质量评价标准评判出各样本图像的最优分割算法,用其构成决策信息表;最后应用粗糙集理论来对决策信息表进行离散化处理和属性约简,以生成图像分割算法选取的决策规则。该决策方法解决了图像跟踪系统中分割算法选取的一系列难题。实验证明,该决策方法能比较有效地根据系统所处理图像的特征选取出算法库中最优的分割算法,并可满足车载图像跟踪系统的实时性要求。

Although there are varieties of image segmentation algorithms, no one is applicable to all images. In order that the image tracking system can select segmentation algorithm itself according to the object image feature, this paper presents an intelligent decision method Of image segmentation. Firstly, some representative segmentation algorithms are selected to form an algorithm library, using which various sample images are segmented; Secondly, decision information table is builtup based on diversified numerical features extracted from the sample images and the optimal segmentation algorithm of each sample image judged according to segmentation quality evaluation criterion; Finally, rough set theory is applied to diseretization and attribution reduction of decision information table, in order to make the decision rule of image segmentation algorithm seiection. The decision method solves a series of problems for segmentation algorithm selection in image tracking system. As experiment shows, it can effectively pick out the optimal segmentation algorithm from algorithm library according to the feature of the processed image, and also satisfy the real-time demand of image tracking system on vehicle.