摘要
针对乳腺X线图像检索过程中底层特征不能有效表达高层语义,提出一种结合视觉语义的图像检索算法。引入图半监督学习框架提取查询图像的类别归属度,作为视觉语义;考虑类别分布的不平衡性,在语义提取过程中加入类标签正则化;设计结合视觉语义和底层特征的相似度度量准则。在乳腺X线分块图像数据库上的实验结果表明,该算法能够有效提取图像的视觉语义,性能优于基于单一底层特征的检索算法。
As low level features can not reflect the high level semantic in mammogram retrieval, an image retrieval algorithm is proposed combining visual semantic. Firstly, a graph-based semi-supervised learning frame is applied to obtain the membership degree of the query images as the visual semantic. The class label regularized method is added to semantic extraction procedure due to the imbalance of class distribution. A similarity measurement is designed combining visual concept and low level features afterwards. The results in mammographic patches database demonstrate that the proposed algorithm can extract the visual se- mantic effectively, and achieve better performance than other retrieval algorithms based on single low level feature.
出处
《计算机工程与设计》
CSCD
北大核心
2013年第9期3163-3167,共5页
Computer Engineering and Design
基金
国家自然科学基金项目(61273251)
南京工业大学青年教师学术基金项目(39709016)
关键词
乳腺X线图像
基于内容的图像检索
视觉语义
图半监督学习
相似度度量
mammography images
content-based image retrieval
visual semantic
graph-based semi-supervised learning
simi-larity measurement