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中文摘要：
提出一种基于粗糙集的图像理解方法．将图像视为一个信息系统，每个像素看作系统中的一个实体对象。引入粗糙集理论中上下近似和核属性的相关概念，采用相容扩展模型下的知识约简方法，对图像处理、分析和解释这3个过程进行分析，提出基于粗糙集的分割算法和知识库规则约简推理方法．通过与Ncuts分割方法及统计学习方法进行理解的实验结果对比，表明算法的可行性和理解的准确性．
英文摘要：
A rough set theory based method for image understanding is proposed. The images are regarded as the information system and each pixel in them as an object in the system . The reduction process and extend models with lower-upper approximations and core attribute concepts in rough sets are considered. Then a segmentation algorithm and a rule reduction and inference method are proposed. The experimental results demonstrate the feasibility and the accuracy of proposed method by comparing it with Ncuts segmentation algorithms and statistical learning ways.
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