Bark classification based on textural features using artificial neural networks

Authors:
Zhi-Kai Huang;Chun-Hou Zheng;Ji-Xiang Du;Yuan-yuan Wan
Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 3
Cited 0

On the Imaging of Fractal Surfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study

IEEE Transactions on Pattern Analysis and Machine Intelligence
Design-based texture feature fusion using Gabor filters and co-occurrence probabilities

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

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.