• DocumentCode
    254617
  • Title

    Joint Shape and Texture Based X-Ray Cargo Image Classification

  • Author

    Jian Zhang ; Li Zhang ; Ziran Zhao ; Yaohong Liu ; Jianping Gu ; Qiang Li ; Duokun Zhang

  • Author_Institution
    Nuctech, Beijing, China
  • fYear
    2014
  • fDate
    23-28 June 2014
  • Firstpage
    266
  • Lastpage
    273
  • Abstract
    Security & Inspection X-Ray Systems is widely used by custom to accomplish some security missions by inspecting import-export cargo. Due to the specificity of cargo X-Ray image, such as overlap, viewpoint dependence, and variants of cargo categories, it couldn´t be understood easily like natural ones by human. Even for experienced screeners, it´s very difficult to judge cargo category and contraband. In this paper, cargo X-Ray image is described by joint shape and texture feature, which could reflect both cargo stacking mode and interior details. Classification performance is compared with the benchmark method by top hit 1, 3, 5 ratio, and it´s demonstrated that good performance is achieved here. In addition, we also discuss X-Ray image property and explore some reasons why cargo classification under X-Ray is very difficult.
  • Keywords
    X-ray imaging; freight handling; image classification; image texture; inspection; shape recognition; tariffs; X-ray cargo image classification; X-ray image property; cargo interior details; cargo stacking mode; image joint shape; image texture feature; inspection; Feature extraction; Image edge detection; Joints; Shape; Stacking; Visualization; X-ray imaging; cargo X-Ray image classification; edge based BOW; joint shape and texture feature;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on
  • Conference_Location
    Columbus, OH
  • Type

    conf

  • DOI
    10.1109/CVPRW.2014.48
  • Filename
    6909993