Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27818
Title: Improvement of image classification using wavelet coefficients with structured-based neural network
Authors: Zou, W
Chi, Z 
Lo, KC
Keywords: Back-Propagation Through Structure (BPTS)
Image classification
Structured-based neural network
Wavelet coefficients
Issue Date: 2008
Publisher: World Scientific Publ Co Pte Ltd
Source: International journal of neural systems, 2008, v. 18, no. 3, p. 195-205 How to cite?
Journal: International Journal of Neural Systems 
Abstract: Image classification is a challenging problem in organizing a large image database. However, an effective method for such an objective is still under investigation. A method based on wavelet analysis to extract features for image classification is presented in this paper. After an image is decomposed by wavelet, the statistics of its features can be obtained by the distribution of histograms of wavelet coefficients, which are respectively projected onto two orthogonal axes, i.e., x and y directions. Therefore, the nodes of tree representation of images can be represented by the distribution. The high level features are described in low dimensional space including 16 attributes so that the computational complexity is significantly decreased. 2800 images derived from seven categories are used in experiments. Half of the images were used for training neural network and the other images used for testing. The features extracted by wavelet analysis and the conventional features are used in the experiments to prove the efficacy of the proposed method. The classification rate on the training data set with wavelet analysis is up to 91%, and the classification rate on the testing data set reaches 89%. Experimental results show that our proposed approach for image classification is more effective.
URI: http://hdl.handle.net/10397/27818
DOI: 10.1142/S012906570800152X
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