基于模糊支持向量机的面向语义图像检索算法

Semantic-based image retrieval algorithm using fuzzy support vector machine

为了缩减图像低层特征和高层语义之间的语义鸿沟,提出一种基于模糊支持向量机的面向语义图像检索(SBIR-FSVM)算法。在提取图像的低层特征的基础上,将最小隶属度模糊支持向量机引入到图像检索技术中,获取图像语义信息及消除传统支持向量机(SVM)在多类分类中产生的不可分区域,从而实现面向语义的图像检索。实验结果表明,提出的SBIR-FSVM算法与基于SVM的图像检索算法及综合多特征的基于内容的图像检索算法相比均有了显著的改进。

To bridge the semantic gap between low-level features and high-level semantics,this paper introduced a new met-hod called semantic-based image retrieval using fuzzy support vector machine（SBIR-FSVM）.By extracting the low-level features of images and introducing the min-membership-function fuzzy support vector machine into image retrieval,obtained the image semantic information and avoided unclassifiable regions in traditional SVM in multi-class classification,thereby,realized semantic-based image retrieval.Experiments show that the SBIR-FSVM algorithm is superior to the image retrieval algorithm based on SVM and the CBIR algorithm using multi-feature.