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Object Recognition Using K-Nearest Neighbor in Object Space

  • Jong-Min Kim
  • Jin-Kyoung Heo
  • Hwan-Seok Yang
  • Mang-Kyu Song
  • Seung-Kyu Park
  • Woong-Ki Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4088)

Abstract

Object recognition technologies using PCA(principal component analysis) recognize objects by deciding representative features of objects in the model image, extracting feature vectors from objects in an image and measuring the distance between them and object representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the K-nearest neighbor technique(class-to-class) in which a group of object models of the same class is used as recognition unit for the images inputted on a continual input image. However, we propose the object recognition technique new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. Object recognition algorithm proposed here represents more enhanced recognition rate to change of illumination than existing methods.

Keywords

Object Recognition Facial Expression Recognition Continual Basis Recognition Unit Extract Feature Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jong-Min Kim
    • 1
  • Jin-Kyoung Heo
    • 2
  • Hwan-Seok Yang
    • 1
  • Mang-Kyu Song
    • 1
  • Seung-Kyu Park
    • 2
  • Woong-Ki Lee
    • 1
  1. 1.Computer Science and Statistic Graduate SchoolChosun UniversityKorea
  2. 2.Division of Cyber Investigation PoliceHowon UniversityKorea

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