基于SVM相关反馈的鞋印图像检索算法  

Relevance Feedback Method Based on SVM in Shoeprint Images Retrieval

作　　者：焦扬 杨传颖[1] 石宝 JIAO Yang;YANG Chuan-ying;SHI Bao(School of Information Engineering,Inner Mongolia University of Technology,Hohhot 010080,China)

机构地区：[1]内蒙古工业大学信息工程学院,呼和浩特010080

出　　处：《计算机科学》2020年第S02期244-247,共4页Computer Science

基　　金：内蒙古自治区自然科学基金(2017BS0602)。

摘　　要：在刑侦方面,鞋印图像的信息化检索对侦破串并案件有着重要的意义。在大规模的鞋印图像库中准确检索出与现场鞋印同类的图像是现在需要解决的问题之一。在基于内容的图像检索基础上,提出一种支持向量机(Support Vector Machine,SVM)与人工反馈结合的方式。利用K-means聚类算法对SIFT(Scale Invariant Feature Transformation)提取的特征向量聚类,构建鞋印图像特征包,并进行相似度排序,得出初步检索结果。用户以此结果进行相关反馈,通过SVM构造相应分类器,最后根据分类结果计算图像与超平面之间的距离来度量图像的相似度排序,返回二次检索结果。实验结果表明,在不同返回结果中二次检索比初步检索的查全率平均提高了6%。In criminal investigation,the information retrieval of shoeprint images is of great significance for the detection of parallel cases.Accurately retrieving images of the same type as on-site shoe prints in a large-scale shoeprint image library is one of the problems that need to be solved now.On the basis of content-based image retrieval,a method combining support vector machine(SVM)and manual feedback is proposed.The K-means clustering algorithm is used to cluster the feature vectors extracted by SIFT(Scale Invariant Feature Transformation),construct the shoeprint image feature package,and sort the similarity to obtain the preliminary retrieval results.The corresponding classifier finally calculates the distance between the image and the hyperplane according to the classification result to measure the similarity of the images and returns the secondary search results.Experimental results show that the recall rate of the secondary search is 6%higher than that of the preliminary search among different returned results.

关 键 词：鞋印图像检索 SIFT K-MEANS 支持向量机 相关反馈 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]