Structured-Based neural network classification of images using wavelet coefficients

Authors:
Weibao Zou;King Chuen Lo;Zheru Chi
Affiliations:
Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong;Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong;Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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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. This paper presents a method based on wavelet for image classification with adaptive processing of data structures. After decomposed by wavelet, the features of an image can be reflected by the wavelet coefficients. Therefore, the nodes of tree representation of images are represented by distribution of histograms of wavelet coefficient projections. 2940 images derived from seven original categories are used in experiments. Half of the images are used for training neural network and the other images used for testing. The classification rate of training set is 90%, and the classification rate of testing set is 87%. The promising results prove the proposed method is very effective and reliable for image classification.