Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 244-247.doi: 10.11896/jsjkx.200400032

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Relevance Feedback Method Based on SVM in Shoeprint Images Retrieval

JIAO Yang, YANG Chuan-ying, SHI Bao   

  1. School of Information Engineering,Inner Mongolia University of Technology,Hohhot 010080,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:JIAO Yang,born in 1995,master candidate.Her main research interests include image processing.
    YANG Chuan-ying,born in 1972,master,associate professor.His main research interests include machine lear-ning and image processing.
  • Supported by:
    This work was supported by the Natural Science Fund Project of Inner Mongolia Autonomous Region(2017BS0602).

Abstract: 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.

Key words: K-means, Relevance feedback, Shoeprint image retrieval, SIFT, SVM

CLC Number: 

  • TP391
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