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.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2007年第2期287-294,共8页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.60375011)
安徽省优秀青年科技基金项目(No.04042044)
新世纪优秀人才支持计划项目(No.NCET-04-0560)
关键词
图像理解
粗糙集
图像分割
规则约简
规则推理
Image Understanding, Rough Set, Image Segmentation, Rules Reduction, Rules Inference
