一种基于SVMS的语义图像分类方法  

Semantic-based image classification using SVMS

作　　者：刘盈盈[1] 石跃祥[1] 莫浩澜[1] 文立[1] 

机构地区：[1]湘潭大学信息工程学院,湖南湘潭411105

出　　处：《计算机应用研究》2008年第2期452-454,共3页Application Research of Computers

基　　金：国家自然科学重点基金资助项目(60234030);国防科工委资助项目

摘　　要：如何跨越图像低层视觉特征到高层语义特征的"语义鸿沟"已成为语义图像检索问题的关键,首先将待分类图像分成五个区域;然后在提取图像底层特征的基础上,采用基于支持向量机组(SVMS)的方法建立图像低层视觉特征到高层语义特征之间的映射,将一幅图像同时归入一类或几类图像语义。实验结果表明,该方法具有较好的检索查全率和准确率。The solution of ＂semantic gap＂ which existence between the low-features and the high-level semantic features had become the key in problems of the semantic image retrieval, First separated the image into five part, then extracted low-level features, used a new approach to establish a link from image low-level feature to high-level semantic based on support vector machines. Finally, the images were classified as one or several classes. The experiment proves that it has obtained the high accuracy.

关 键 词：语义图像检索 底层特征 高层语义 支持向量机 

分 类 号：TP317.4[自动化与计算机技术—计算机软件与理论]