Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 255-259.doi: 10.11896/jsjkx.200900033

• Big Data & Data Science • Previous Articles     Next Articles

Footprint Image Clustering Method Based on Automatic Feature Extraction

CHEN Yang1,2, WANG Jin-liang3, XIA Wei3, YANG Hao1,2, ZHU Run3, XI Xue-feng1,2   

  1. 1 School of Electronic & Information Engineering,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China
    2 Suzhou Virtual Reality Intelligent Interaction and Application Technology Key Laboratory,Suzhou,Jiangsu 215009,China
    3 Kunshan Public Security Bureau,Kunshan,Jiangsu 215300,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:CHEN Yang,born in1994,postgra-duate.His main research interests include deep learning and computer vision.XI Xue-feng,born in 1978,Ph.D,asso-ciate professor,master supervisor.His main research interests include natural language processing and machine lear-ning.
  • Supported by:
    National Natural Science Foundation of China(61876217),InnovativeTeam of Jiangsu Province(XYDXX-086) and Postgraduate Research & Practice Innovation Program of Suzhou University of Science and Technology(SKSJ18_010).

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Abstract: Footprint images are the most important clues in the detection process of public security cases.Every year,public security agencies collect many crime scene footprints.How to automatically organize and categorize these footprint images has become a difficulty for public security informatization.To meet the actual needs of public security,this paper combines a convolutional neural network and DBSCAN algorithm to propose a method for clustering footprint images.First,the footprint image is preprocessed to meet the model training requirements.Then,through model pre-training,the two types of Resnnet50 and Densenet121 convolutional neural network model structures are improved to extract footprint image features and establish a feature vector library.Based on DBSCAN Similar algorithms,we use the above feature vector library to organize and classify footprint images.Experimental results show that the method has good practicability and effectiveness.

Key words: Automatic feature extraction, Convolutional neural network, Footprint image, Image clustering

CLC Number: 

  • TP181
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