摘要
分析了基于内容的图像检索中存在的问题,利用本体论方法建立图像底层特征本体及特定类图像本体.同时,定义了图像描述因子并建立相应的图像组合规则.最后,利用图像的底层特征进行图像检索,结合多分类支持向量机,实现图像底层特征与高层描述信息的关联,进而实现了图像语义检索,缩小了"语义鸿沟"对基于内容的图像检索的影响.实验结果表明本模型能够提高基于内容的图像检索的准确率,同时,经过3～5次反馈,可以实现语义检索功能.
The problems in the content-based image retrieval are analyzed,and then the ontology theory method is used to build the low-level feature ontology of the image and the special image ontology.Meanwhile,the concept of image descriptor is defined and corresponding image combination rules are setup.At last,the low-level features are selected to retrieve the images,and the multi-class support vector machine（SVM） is chosen to achieve the conjunction between the low-level feature and the high-level description information.Then the semantic retrieval is realized and the influence of the semantic gap problem on the content based image retrieval is reduced.The experiment results show that this model can enhance the precision in content based image retrieval,and it can achieve the semantic retrieval by 3～5 times of feedback.
出处
《信息与控制》
CSCD
北大核心
2012年第3期319-325,共7页
Information and Control
关键词
图像检索
本体
图像描述
多分类支持向量机
image retrieval
ontology
image description
multi-class support vector machine
