摘要
为提高数字图书馆的资源整合以及对图像的分析能力,介绍了一种基于内容的图像检索方法,该方法基于特征包(Bagoffeatures)方法,提取图像的SURF特征作为视觉单词,之后运用建立视觉字典,通过直方图表示图像特征,最终实现图像的检索,实践表明该系统具有良好的运算效率以及检索精度,并且充分考虑到图像的光照、透视、图像尺寸大小等因素,可以促进与改进图书馆的工作流程,也可以根据工作需求进行对应的技术改进,适合在数字图书馆进行进一步推广。
In order to improve the ability of resource integration and image analysis in digital libraries, this paper proposes a content-based image retrieval method, which extracts SURF features of images as visual words based on bag of features method and uses K-means method to build a visual dictionary. It then obtains histogram features of images according to the mapping relationships between visual words and visual dictionary. At last, the similarity between histograms is used for image retrieval. Practice shows that the system has good operational efficiency and retrieval accuracy, and fully considers the illumination, perspective, image size and other factors. It can not only promote and improve the working process in library but also make corresponding technical improvement according to specific needs, which is suitable for further promotion in digital libraries.
作者
包翔
刘桂锋
Bao Xiang;Liu Guifeng(Institute of Science and Technology Information,Jiangsu University)
出处
《图书馆杂志》
CSSCI
北大核心
2020年第8期57-65,共9页
Library Journal
基金
江苏省高校哲学社会科学研究一般项目“主题模型在高校图书馆知识产权信息服务中的研究与实践”(项目编号:2019SJA1870)
江苏省高校自然科学研究面上项目“基于多示例多标签学习及深度神经网络的专利主题分类研究”(项目编号:19KJB520005)的研究成果之一。
