A New Content Based Image Retrieval Method Using Contourlet Transform



1 Multimedia Systems Research Group, IT Research Institute, Iran Telecom Research Center, Tehran, Iran

2 Department of Computer Engineering, Science & Research Branch, Islamic Azad University, Tehran, Iran


One of the challenging issues in managing the existing large digital image libraries and databases is Content Based Image Retrieval (CBIR). The accuracy of image retrieval methods in CBIR is subject to effective extraction of image features such as color, texture, and shape. In this paper, we propose a new image retrieval method using contourlet transform coefficients to index texture of the images. We employ the properties of contourlet coefficients to model the distribution of coefficients in each sub-band using the normal distribution function. The assigned normal distribution functions are used effectively at the next stage to extract the texture feature vector. Simulation results indicate that the proposed method outperforms other conventional texture image retrieval methods such as, Gabor filter and wavelet transform. Moreover, this method shows a noticeable higher performance compared to another contourlet based CBIR method.


[1] A. Laine, and J. Fan, Texture classification by wavelet packet signatures. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1186-1191, 1993.
[2] T. Chang and C.C.J. Kuo, Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. on Image Processing, vol. 2, no. 4, pp. 429-441, 1993.
[3] M. N. Do and M. Vetterli, Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Trans. on Image Processing, vol. 11, no. 2, pp. 146-158, 2002.
[4] L. Brigale, M. Kokare and D. Doye, Colour and texture features for content based image retrieval. in Proc. of Int. Conf. on Computer Graphics, Imaging and Visualisation, Sysdney, pp. 146-149, 2006.
[5] M. K. Mandal and C. Liu, Efficient image indexing techniques in the JPEG2000 domain. Journal of Electronic Imaging, vol. 13, pp. 182-187, 2004.
[6] F. Zargari, A. Mosleh and M. Ghanbari, A fast and efficient compressed domain JPEG2000 image retrieval method. IEEE Trans. on Consumer Electronics, vol. 54, no. 4, pp. 1886-1893, 2008.
[7] B. S. Manjunath and W. Y. Ma, Texture features for browsing and retrieval of image data. IEEE T-PAMI special issue on Digital Libraries, vol. 18, no. 8, pp. 837-842, 1996.
[8] D. Zhang and G. Lu, Content-based image retrieval using Gabor texture features. Proc. of First IEEE Pacific- rim Conf. on Multimedia (PCM”00), Fargo, ND, USA, pp.1-9, 2001.
[9] M. N. Do and M. Vetterli, The contourlet transform: An efficient directional multiresolution image representation. IEEE Trans. on Image Processing, vol. 14, no. 12, pp. 2091-2106, 2005.
[10] Z. Longa and N. H. Younana, Contourlet spectral histogram for texture classification. Proc. of 2006 IEEE Southwest Symp. on Image Analysis and Interpretation, pp. 31-35, 2006.
[11] R. Romdhane, H. Mahersia and K. Hamrouni, A novel content image retrieval method based on contourlet. Proc. of 3rd International Conf. on Information and Communication Technologies: From Theory to Applications, pp. 1-5, 2008.
[12] M. N. Do and M. Vetterli, Directional multiscale modeling of images using the contourlet transform. IEEE Trans. on Image Processing, vol. 15, no. 6, pp. 1610-1620, 2006.
[13] A. Mosleh and F. Zargari, Texture image retrieval using contourlet transform. Accepted for Publication in Proc. of 9th International Symposium on Signals, Circuits and Systems, Romania, 2009.
[14] http://vismod.media.mit.edu/vismod/imagery/VisionTexture
[15] Y. Gong, H. J. Zhang and T. C. Chua, An image database system with content capturing and fast image indexing abilities. Proc. IEEE Int. Conf. on Multimedia Computing and Systems, Boston, pp.121-130, 14-19 May 1994.
[16] L. Chen, G. Lu and D. S. Zhang, Effects of different Gabor filter parameters on image retrieval by texture. Proc. of IEEE Int. Conf. on Multi-Media Modeling, pp. 273-278, 2004.
[17] C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval. Cambridge University Press, 2008.