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).

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
[1] DICKENSON M,GUEGUEN L.Rotated Rectangles for Symbolized Building Footprint Extraction[C]//IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition Workshops.2018:225-228.
[2] SUN Y,WANG Y Y,ZHU X X.Automatic Registration of SAR Image and GIS Building Footprints Data in Dense Urban Area[C]//2019 IEEE International Geoscience and Remote Sensing Symposium.2019:927-930.
[3] LI Z S.Simulation Study on the Rapid Identification of Footprint Image of Aerobics Athletes[J].Computer Simulation,2017,34(3):221-224.
[4] CHEN Y,ZENG C,CHENG C,et al.A CNN-based Approach to Footprint Image Retrieval and Matching[J].Journal of NanJing Normal University(Engineering and Technology Edition),2018,18(3):45-51.
[5] ZHOU F Y,JIN L P,DONG J.Review of Convolutional Neural Network[J].Chinese Journal of Computers,2017,40(6):1229-1251.
[6] LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.
[7] CHEN S D,WEI W,HE B Q,et al.Action recognition base on improved deep convolutional neural network[J].Application Research of Computers,2019(3):62.
[8] HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[9] HUANG G,LIU Z,VAN DER MAATEN L,et al.Densely con-nected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4700-4708.
[10] WANG J H,ADRIAN H,JIANG J M.Spectral Analysis Network for Deep Representation Learning and Image Clustering[C]//2019 IEEE International Conference on Multimedia and Expo (ICME).2019:1540-1545.
[11] SCHUBERT E,SANDER J,ESTER M,et al.DBSCAN revisited,revisited:why and how you should (still) use DBSCAN[J].ACM Transactions on Database Systems (TODS),2017,42(3):1-21.
[12] NEWMAN M E J,CANTWELL G T,YOUNG J G.Improved mutual information measure for clustering,classification,and community detection[J].Physical Review E,2020,101(4):23-34.
[13] SUN S L,WANG C,ZHAO Y D.Parameter independent clustering of air traffic trajectory based on silhouette coefficient[J].Journal of Computer Applications,2019,39(11):3293-3297.
[14] QIAN X D,LUO Y F.Incomplete Data Clustering Algorithm Based on Mutual Information Attributes Ranking[J].Information and Control,2019,48(1):84-91.
[15] XIE J Y,ZHOU Y,WANG M Z,et al.CAAI Transactions on Intelligent Systems[J].New Criteria for Evaluating the Validity of Clustering,2017,12(6):873-882.
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