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Hierarchical Classification of Object Images Using Neural Networks

  • Jong-Ho Kim
  • Jae-Won Lee
  • Byoung-Doo Kang
  • O-Hwa Kwon
  • Chi-Young Seong
  • Sang-Kyoon Kim
  • Se-Myung Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

We propose a hierarchical classifier of object images using neural networks for content-based image classification. The images for classification are object images that can be divided into foreground and background areas. In the preprocessing step, we extract the object region and shape-based texture features extracted from wavelet-transformed images. We group the image classes into clusters that have similar texture features using Principal Component Analysis (PCA) and K-means. The hierarchical classifier has five layers that combine the clusters. The hierarchical classifier consists of 59 neural network classifiers that were learned using the back-propagation algorithm. Of the various texture features, the diagonal moment was the most effective. A test showed classification rates of 81.5% correct with 1000 training images and of 75.1% correct with 1000 test images. The training and test sets each contained 10 images from each of 100 classes.

Keywords

Texture Feature Object Image Object Region Neural Network Classifier Human Classifier 
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-Ho Kim
    • 1
  • Jae-Won Lee
    • 1
  • Byoung-Doo Kang
    • 1
  • O-Hwa Kwon
    • 1
  • Chi-Young Seong
    • 1
  • Sang-Kyoon Kim
    • 1
  • Se-Myung Park
    • 1
  1. 1.Department of Computer ScienceInje UniversityKimhaeKorea

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