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A Study of the Effect of Color Quantization Schemes for Different Color Spaces on Content-based Image Retrieval

by Moheb R. Girgis, Mohammed S. Reda
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 12
Year of Publication: 2014
Authors: Moheb R. Girgis, Mohammed S. Reda
10.5120/16843-6699

Moheb R. Girgis, Mohammed S. Reda . A Study of the Effect of Color Quantization Schemes for Different Color Spaces on Content-based Image Retrieval. International Journal of Computer Applications. 96, 12 ( June 2014), 1-8. DOI=10.5120/16843-6699

@article{ 10.5120/16843-6699,
author = { Moheb R. Girgis, Mohammed S. Reda },
title = { A Study of the Effect of Color Quantization Schemes for Different Color Spaces on Content-based Image Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 12 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume96/number12/16843-6699/ },
doi = { 10.5120/16843-6699 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:21:32.284878+05:30
%A Moheb R. Girgis
%A Mohammed S. Reda
%T A Study of the Effect of Color Quantization Schemes for Different Color Spaces on Content-based Image Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 12
%P 1-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Color spaces, color histograms, histogram distance measurements, size and quantization play an important role in retrieving images based on similarities. This paper presents a study of the effect of color quantization schemes for different color spaces (HSV, YIQ and YCbCr) on the performance of content-based image retrieval (CBIR), using different histogram distance measurements (Histogram Euclidean Distance and Histogram Intersection Distance). For the purpose of this study, a CBIR system that implements two content-based image retrieval algorithms has been developed. The first algorithm is based only on the color feature, while the second one is based on combination of the color and texture features. The color histogram is used for image color feature extraction and Haar wavelet transform is used for image texture feature extraction. The WANG image database, which contains 1000 general-purpose color images, has been used in the experiments of this study. The experimental results show which histogram distance measurement is best, which color space gives better retrieval precision, and the best quantization schemes for the considered color spaces, when using only the color feature, and when using a combination of the color and texture features.

References
  1. Rasheed, W. 2008. Sum of Values of Local Histograms for Image retrieval. Chosun University, Gwangju, South Korea.
  2. Wang, B. 2008. A Semantic Description For Content-Based Image Retrieval. College Of Mathematics And Computer Science, Hebei University, Baoding 071002, China.
  3. Zhang, D. 2004. Improving image retrieval performance by using both color and texture features. In Proc. 3rd Int. Conf. Image Graph. , Hong Kong, pp. 172–175.
  4. Singha, M. and Hemachandran, K. 2012. Content Based Image Retrieval Using Color and Texture. Signal & Image Processing: An International Journal (SIPIJ) Vol. 3, No. 1, pp. 39-57.
  5. Fuertes, J. M. , Lucena, M. , Blanca, N. P. D. L. , and Martinez, J. C. 2001. A Scheme of Color Image Retrieval from Databases. Pattern Recognition, pp. 323-337.
  6. Chan, Y. K. and Chen, C. Y. 2004. Image retrieval system based on color-complexity and color-spatial features. The Journal of Systems and Software, pp. 65-70.
  7. Swain, M. and Ballard, D. 1991. Color indexing. International Journal of Computer Vision, pp. 11–32.
  8. Wang, J. Z. 2001. Integrated Region-Based Image Retrieval. Boston, Kluwer Academic Publishers.
  9. Smeulders, A. M. W. , Worring, M. , Santini, S. , Gupta, A. , and Jain, R. 2000. Content based image retrieval at the end of the early years. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, pp. 1349-1380.
  10. Vailaya, A. , Figueiredo, M. A. G. , Jain, A. K. , and Zhang, H. J. 2001. Image classification for content-based indexing. IEEE Trans. on Image Processing, Vol. 10, No. 1.
  11. Color spaces - RGB, CMYK, and YCbCr, http://www. rw-designer. com/color-space, Last accessed 4/2014.
  12. Flickner, M. , Sawhney, H. , Niblack, W. , Ashley, J. , Huang, Q. , Dom, B. , Gorkani, M. , Hafne, J. , Lee, D. , Petkovic, D. , Steele, D. , and Yanker, P. 1995. Query by Image and Video Content The QBIC System. IEEE Computer, pp-23-32.
  13. Broek, E. L. , den, van 2005. Human-Centered Content-Based Image Retrieval. Ph. D. thesis Nijmegen Institute for Cognition and Information (NICI), Radboud University Nijmegen, The Netherlands – Nijmegen.
  14. Smith, J. R. 1997. Integrated spatial and feature image system: Retrieval, analysis and compression, Ph. D. dissertation, Columbia University, New York.
  15. Wan X. and Jay Kuo, C. -C. 1996. Image retrieval with multiresolution color space quantization. Proc. SPIE 2898, Electronic Imaging and Multimedia Systems, 148, September 30.
  16. Wan, X. and Jay Kuo, C. -C. 1996 -. Color distribution analysis and quantization for image retrieval. Proc. SPIE 2670, Storage and Retrieval for Still Image and Video Databases IV, 8 (March 13, 1996), pp. 9- 16.
  17. Zhang, Z. , Li, W. , Li, B. 2009 -. An Improving Technique of Color Histogram in Segmentation-based Image Retrieval. At Fifth International Conference on Information Assurance and Security. IEEE.
  18. Smith, J. R. and Chang, S. F. 1996. Tools and techniques for color image retrieval. IST/SPIE-Storage and Retrieval for Image and Video Databases IV, San Jose, CA, 2670, 426-437.
  19. IEEE 1990. IEEE standard glossary of image processing and pattern recognition terminology. IEEE.
  20. Smith, J. R. and Chang, S. 1994. Transform Features for Texture Classification and Discrimination in Large Image Databases. Proceeding of IEEE International Conference on Image Processing, pp. 407-411.
  21. Manjunath, B. , Wu, P. , Newsam, S. , and Shin, H. 2000. A texture descriptor for browsing and similarity retrieval. Journal of Signal Processing: Image Communication, pp. 33-43.
  22. Haralick, R. 1979. Statistical and structural approaches to texture. IEEE, pp. 786–804.
  23. Tamura, H. , Mori, S. , and Yamawaki, T. 1978. Textural features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybern, pp. 460-472.
  24. Wouwer, G. V. D. , Scheunders P. , and Dyck, D. V. 1999. Statistical texture characterization from discrete wavelet representation. IEEE Transactions on Image Processing, Vol. 8, pp-592–598.
  25. Livens, S. , Scheunders, P. , Wouwer, G. V. D. , and Dyck, D. V. 1997. Wavelets for texture analysis, an overview. Sixth International Conference on Image Processing and Its Applications, pp. 581–585.
  26. Daubechies. I. 1992. Ten lecturer on wavelet". Philadelphia, PA: Society for Industrial and Applied Mathematics Analysis, vol. 23, pp. 1544–1576.
  27. Mallet, S. 1996. Wavelets for a Vision. Proceeding to the IEEE, Vol. 84, pp. 604-685.
  28. Haar, A. 1910. Zur Theorier der Orthogonalen Funktionensystem. Math. Annal. , Vol. 69, pp-331-371.
  29. WANG Databases. http://wang. ist. psu. edu/docs/related/.
  30. Wang, J. Z. and Li, J. 2001. SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 9.
Index Terms

Computer Science
Information Sciences

Keywords

Histogram-based image retrieval Color quantization Color spaces Precision Histogram similarity measures.

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