摘要
提出一种基于本体的图像检索方法,该方法首先采用改进的K均值无监督分割方法将图像分割成区域,然后提取每个区域的颜色、形状、位置、纹理等低层描述特征,应用这些特征定义一个简单的对象本体。为了提高图像检索的准确度,最后采用支持向量机(SVM)的相关反馈算法。实验结果表明,提出的方法不仅可以提高检索效率,而且对于缩小低层视觉特征和高层语义特征之间的“语义鸿沟”具有很大的意义。
A new image retrieval method using ontology is presented in this paper.First,the proposed approach employs a improved K-mean and fully unsupervised segmentation algorithm to divide images into regions,and low-level descriptors for the color,shape,position,and texture of each region are subsequently extracted.And then define a object ontology using these features. In order to improve the precision of image retrieval,a relevance feedback mechanism,based on support vector machines is invoked.The experiment results show that the proposed approach not only has an excellent precision but also has strong significance for reducing the ＂semantic gap＂ between the visual feature and semantic visual.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第17期37-40,共4页
Computer Engineering and Applications
基金
国家自然科学基金(the National Natural Science Foundation of China under Grant No.79816101)
关键词
图像检索
本体
图像分割
相关反馈
支持向量机
image retrieval
ontology
image segmentation
relevance feedback
support vector machines
