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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cohen, A., Ryan, R.D.: Wavelets and Multiscale Signal Processing. Chapman & Hall Press, Boca Raton (1995)
Giles, C.L., Gori, M.: Adaptive Processing of Sequences and Data Structures. Springer, Berlin (1998)
Tsoi, A.C.: Adaptive Processing of Data Structures: An Expository Overview and Comments. Technical report, Faculty of Informatics, University of Wollongong, Australia (1998)
Tsoi, A.C., Hangenbucnher, M.: Adaptive Processing of Data Structures. Keynote Speech. In: Proceedings of Third International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999, 2-2 (summary) (1999)
Frasconi, P., Gori, M., Sperduti, A.: A General Framework for Adaptive Processing of Data Structures. IEEE Trans. on Neural Networks 9, 768–785 (1998)
Sperduti, A., Starita, A.: Supervised Neural Networks for Classification of Structures. IEEE Trans. on Neural Networks 8(3), 714–735 (1997)
Goller, C., Kuchler, A.: Learning Task-dependent distributed representations by Back-propagation Through Structure. In: Proc. IEEE Int. Conf. Nerual Networks, pp. 347–352 (1996)
Cho, S., Chi, Z., Wang, Z., Siu, W.: An Efficient Learning Algorithm for Adaptive Processing of Data Structure. Neural Processing Letters 17, 175–190 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zou, W., Lo, K.C., Chi, Z. (2006). Structured-Based Neural Network Classification of Images Using Wavelet Coefficients. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_48
Download citation
DOI: https://doi.org/10.1007/11760023_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)