A New Content Based Image Retrieval Model Based on Wavelet Transform


Searching interested images based on visual properties of images is a challenging problem and it has received considerable attention from researchers in the fields like image processing, computer vision and multimedia systems in the last 20 years. While the importance and the effect of the image features like color, texture and shape have been taken into account in many papers, there have not been many studies on the importance of the color spaces on the performance of Content Based Image Retrieval (CBIR) systems. In this paper we first experimentally study the effect of choosing color space on the performance of content based image retrieval using Wavelet decomposition of each color channel. To this end, the retrieval results of different color spaces like RGB, YUV, HSV, YCbCr and Lab are analyzed. Then as a result a new Content Based Retrieval model using Wavelet Transform in Lab color space and Color Moments is proposed. In order to increase the efficiency of the proposed model some division schemes are taken into account which improves the performance of the proposed model. The proposed model tackles one of the important restrictions in content based image retrieval, namely, the challenge between the accuracy of retrieval and its time complexity. The experimental results on two databases [19] [24] demonstrate the superiority of the proposed model compared to existing models.

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Giveki, D. , Soltanshahi, A. , Shiri, F. and Tarrah, H. (2015) A New Content Based Image Retrieval Model Based on Wavelet Transform. Journal of Computer and Communications, 3, 66-73. doi: 10.4236/jcc.2015.33012.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Kherfi, M.L., Ziou, D. and Bernardi, A. (2004) Image Retrieval from the World Wide Web: Issues, Techniques, and Systems. ACM Computing Surveys, 36, 35-67. http://dx.doi.org/10.1145/1013208.1013210
[2] Datta, R., Joshi, D., Li, J. and Wang, J.Z. (2008) Image Retrieval: Ideas, Influences, and Trends of the NEW Age. ACM Computing Surveys, 40, 1-60.
[3] Yang, M., Kpalma, K. and Ronsin, J. (2010) A Survey of Shape Feature Extraction Techniques. Pattern Recognition, 1-38.
[4] Penatti Otavio, A.B., Valle, E. and Torres, R.da.S. (2012) Comparative Study of Global Color and Texture Descriptors for Web Image Retrieval. Int. J. Via.Commun. Image R, 359-380.
[5] Deselaers, T., Keysers, D. and Ney, H. (2008) Features for Image Retrieval: An Experimental Comparison. Information Retrieval, 11, 77-107.
[6] Mallat, S.G. (1989) A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674-693.
[7] Sarck, J.L., Murtagh, F.D. and Bijaoui, A. (1998) Image Processing and Data Analysis: The Multiscale Approach.
[8] Hill, P., Achim, A. and Bull, D. (2012) The Undecimated Dual Tree Complex Wavelet Transform and Its Application to Bivariate Image Denoising Using a Cauchy Model. 19th IEEE International Conference on Image Processing (ICIP), 1205-1208. http://dx.doi.org/10.1109/icip.2012.6467082
[9] Kalra, M. and Ghosh, D. (2012) Image Compression Using Wavelet Based Compressed Sensing and Vector Quantization. IEEE 11th International Conference on Signal Processing (ICSP), 1, 640-645.
[10] Kokareh, M., Biswas, P.K. and Chatterji, B.N. (2005) Texture Image Retrieval Using New Rotated Complex Wavelet Filters. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 35, 1168-1178.
[11] Balamurugan, V. and Anandha Kumar, P. (2008) An Integrated Color and Texture Feature Based Framework for Content Based Image Retrieval Using 2D Wavelet Transform. IEEE International Conference on Computing, Communication and Networking, 1-16. http://dx.doi.org/10.1109/icccnet.2008.4787734
[12] Quellec, G., Lamard, M., Cazuguel, G., Cochener, B. and Roux, C. (2012) Fast Wavelet-Based Image Characterization for Highly Adaptive Image Retrieval. IEEE Transactions on Image Processing, 21, 1613-1623.
[13] Agarwal, S., Verma, A.K. and Singh, P. (2013) Content Based Image Retrieval Using Discrete Wavelet Transform and Edge Histogram Descriptor. International Conference on Information Systems and Computer Networks (ISCON), 19- 23. http://dx.doi.org/10.1109/iciscon.2013.6524166
[14] Wang, Y. and Zhang, W. (2012) Coherence Vector Based on Wavelet Coefficients for Image Retrieval. IEEE International Conference on Computer Science and Automation En-gineering (CSAE), 2, 765-768. http://dx.doi.org/10.1109/CSAE.2012.6272878
[15] Hu, J.-L., Deng, J.-B. and Sui, M.-X. (2009) Color Space Con-version Model from CMYK to LAB Based on Prism. IEEE International Conference on Granular Computing, 235-238. http://dx.doi.org/10.1109/grc.2009.5255123
[16] Pratt, W.K. (2001) Digital Image Processing. 3rd Edition, PIKS Inside. Wiley. http://dx.doi.org/10.1002/0471221325
[17] Foley, J.D., van Dam, A., Feiner, S.K., Hughes, J.F. and Phillips, R.L. (1993) Introduction to Computer Graphics. Addison-Wesley Longman, Amsterdam.
[18] Ford, A. and Roberts, A. (1998) Color Space Conversions.
[19] Wang Database. http://wang.ist.psu.edu/docs/related.shtml
[20] Meng, F., Guo, B. and Fang, Y. (2010) Novel Image Retrieval Model based on Interest Points. 3rd International Congress on Image and Signal Processing CISP, 1582-1585.
[21] Lin, Ch.-H., Chen, R.-T. and Chan, Y.-K. (2009) A Smart Content Based Image Retrieval System Based on Color and Texture Features. Image and Vision Computing, 27, 658-665.
[22] Huang, P.W. and Dai, S.K. (2003) Image Retrieval by Texture Similarity. Pattern Recognition, 36, 665-679.
[23] Jhanwar, N., Chaudhurib, S., Seetharamanc, G. and Zavidovique, B. (2004) Content Based Image Retrieval Using Motif Co-Occurrence Matrix. Image and Vision Computing, 22, 1211-1220.
[24] Li’s Database. http://sites.stat.psu.edu/~jiali/index.download.html

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