Contourlet-S Retrieval Algorithm Using Absolute Mean Energy and Kurtosis Features

Abstract:

Contourlet transform has better performance in directional information representation than wavelet transform and has been studied by many researchers in retrieval systems and has been shown that it is superior to wavelet ones at retrieval rate. In order to improve the retrieval rate further, a contourlet-S transform based texture image retrieval system was proposed in this paper. In the system, the contourlet transform was constructed by non-subsampled Laplacian pyramid cascaded by critical subsampled directional filter banks, sub-bands absolute mean energy and kurtosis in contourlet-S domain are cascaded to form feature vectors, and the similarity metric is Canberra distance. Experimental results on 109 brodatz texture images show that using the features cascaded by absolute mean and kurtosis can lead to a higher retrieval rate than the combination of standard deviation and absolute mean which is most commonly used today under same dimension of feature vectors. contourlet-S transform based image retrieval system is superior to those of the original contourlet transform, non-subsampled contourlet system under the same system structure with same length of feature vectors, retrieval time and memory needed, contourlet-S decomposition structure parameters can make significant effects on retrieval rates, especially scale number.

• Periodical: Applied Mechanics and Materials (Volume 197)
• Pages: 473-476
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.197.473
• Online since: September 2012
• Authors: Xiang Ying Li, Rui Xue, Xin Wu Chen, Wei Luo
• Keywords: Content Based Image Retrieval, Contourlet Transform, Contourlet-S Transform, Non-Subsampled Contourlet Transform, Retrieval Rate, Texture Image

References

[1] A. Smeulders, M. Worring and S. Santini: Content-Based Image Retrieval at the End of the Early Years. IEEE Trans. Pattern Recognit. Machine Intell. Vol. 22 (2000), pp.1349-1380.

DOI: 10.1109/34.895972

[2] N.D. Minh, M. Vetterli: Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance. IEEE Transactions on Image Processing. Vol. 11 (2002), pp.146-158.

DOI: 10.1109/83.982822

[3] A. Laine, J. Fan: Texture Classification by Wavelet Packet Signatures, IEEE Trans. Pattern Recognit. Machine Intell. Vol. 15 (1993), pp.1186-1191.

DOI: 10.1109/34.244679

[4] T. Chang, C. Kuo: Texture Analysis and Classification with Tree-Structure Wavelet Transform, IEEE Trans. on Image Processing. Vol. 2 (1993) pp.429-441.

DOI: 10.1109/83.242353

[5] N.D. Minh, M. Vetterli: The Contourlet Transform: An Efficient Directional Multiresolution Image Representation. IEEE Transactions on Image Processing. Vol. 14 (2005), pp.2091-2106.

DOI: 10.1109/tip.2005.859376

[6] D. Cunha, J. Zhou, M. N. Do: The Nonsubsampled Contourlet Transform: Theory, Design, and Applications, IEEE Transactions on Image Processing, Vol. 15 (2006), pp.3089-3101.

DOI: 10.1109/tip.2006.877507

[7] K.S. Arun, P.M. Hema: Content Based Medical Image Retrieval by Combining Rotation Invariant Contourlet Features and Fourier Descriptors, International Journal of Recent Trends in Engineering, Vol. 2 (2009), pp.35-39.

[8] Brodatz Texture Images on http: /www. ux. uis. no/~tranden/brodatz. html.