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Bark Classification Based on Textural Features Using Artificial Neural Networks

  • Zhi-Kai Huang
  • Chun-Hou Zheng
  • Ji-Xiang Du
  • Yuan-yuan Wan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

In this paper, a new method for bark classification based on textural and fractal dimension features using Artificial Neural Networks is presented. The approach involving the grey level co-occurrence matrices and fractal dimension is used for bark image analysis, which improves the accuracy of bark image classification by combining fractal dimension feature and structural texture features on bark image. Furthermore, we have investigated the relation between Artificial Neural Network (ANN) topologies and bark classification accuracy. Furthermore, the experimental results show the facts that this new approach can automaticly identify the Tplants categories and the classification accuracy of the new method is better than that of the method using the nearest neighbor classifier.

Keywords

Fractal Dimension Texture Feature Gabor Filter Average Recognition Rate Texture Feature Extraction 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhi-Kai Huang
    • 1
    • 2
  • Chun-Hou Zheng
    • 1
    • 2
  • Ji-Xiang Du
    • 1
    • 2
  • Yuan-yuan Wan
    • 1
  1. 1.Intelligent Computing Lab, Hefei Institute of Intelligent MachinesChinese Academy of SciencesHefei, AnhuiChina
  2. 2.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina

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