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Integration of Color and Texture Features in CBIR System

by Hany F. Atlam, Gamal Attiya, Nawal El-Fishawy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 164 - Number 3
Year of Publication: 2017
Authors: Hany F. Atlam, Gamal Attiya, Nawal El-Fishawy
10.5120/ijca2017913600

Hany F. Atlam, Gamal Attiya, Nawal El-Fishawy . Integration of Color and Texture Features in CBIR System. International Journal of Computer Applications. 164, 3 ( Apr 2017), 23-29. DOI=10.5120/ijca2017913600

@article{ 10.5120/ijca2017913600,
author = { Hany F. Atlam, Gamal Attiya, Nawal El-Fishawy },
title = { Integration of Color and Texture Features in CBIR System },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 164 },
number = { 3 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 23-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume164/number3/27464-2017913600/ },
doi = { 10.5120/ijca2017913600 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:10:16.558088+05:30
%A Hany F. Atlam
%A Gamal Attiya
%A Nawal El-Fishawy
%T Integration of Color and Texture Features in CBIR System
%J International Journal of Computer Applications
%@ 0975-8887
%V 164
%N 3
%P 23-29
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays, rapid and effective searching for relevant images in large image databases has become an area of wide interest in many applications. The current image retrieval system is based on text-based approaches. This system has many challenges such as it cannot retrieve images that are context sensitive and the amount of effort required to manually annotate every image, as well as the difference in human perception when describing the images, which result in inaccuracies during the retrieval process. Content-based image retrieval (CBIR) supports an effective way to retrieve images depending on automatically derived image features. It retrieves relevant images using unique image features such as texture, color or shape. This paper presents novel methods to retrieve relevant images from large image databases. Two proposed methods are presented. The first proposed method improves the retrieval performance by identifying the most efficient gray-level co-occurrence matrix (GLCM) texture features and combine them with the appropriate Discrete Wavelet Transform (DWT) decomposition band. The second proposed method increases the system performance by combining color and texture features as one feature vector which is resulting in increasing the retrieval accuracy. The proposed methods have shown a promising and faster retrieval on a WANG image database containing 1000 color images. The retrieval performance has been evaluated with the existing systems that discussed in the literature. The proposed methods give better performance than other systems.

References
  1. S. Pattanaik and D. Bhalke, “Beginners to Content-Based Image Retrieval,” Ijsret.Org, vol. 1, no. May, pp. 40–44, 2012.
  2. Y. Chen, J. Z. Wang, and R. Krovetz, “CLUE: Cluster-based retrieval of images by unsupervised learning,” IEEE Trans. Image Process., vol. 14, no. 8, pp. 1187–1201, 2005.
  3. J. Z. Wang, J. Li, and G. Wiederholdy, “SIMPLIcity: Semantics-sensitive integrated matching for picture libraries,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 1929, pp. 360–371, 2000.
  4. Hany F. Atlam, G. Attiya, and N. El-Fishawy, “Comparative Study on CBIR based on Color Feature,” Int. J. Comput. Appl., vol. 78, no. 16, pp. 975–8887, 2013.
  5. E. L. Hall et al., “A Survey of Preprocessing and Feature Extraction Techniques for Radiographic Images,” IEEE Trans. Comput., vol. C-20, no. 9, pp. 1032–1044, 1971.
  6. F. Z. Rangari and U. Ramarao, “Searching by Content based image retrieval through combined features,” vol. 1, no. 2, pp. 85–93, 2013.
  7. J. Afifi Ahmed and M. Ashour Wesam, “Image Retrieval Based on Content using Color Feature,” ISRN Comput. Graph., vol. 341–342, pp. 560–564, 2012.
  8. N. Jhanwar, S. Chaudhuri, G. Seetharaman, and B. Zavidovique, “Content based image retrieval using motif cooccurrence matrix,” Image Vis. Comput., vol. 22, no. 14, pp. 1211–1220, 2004.
  9. P. W. Huang and S. K. Dai, “Image retrieval by texture similarity,” Pattern Recognit., vol. 36, no. 3, pp. 665–679, 2003.
  10. C.-H. Lin, R.-T. Chen, and Y.-K. Chan, “A smart content-based image retrieval system based on color and texture feature,” Image Vis. Comput., vol. 27, no. 6, pp. 658–665, 2009.
  11. M. B. Rao, B. P. Rao, and A. Govardhan, “CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features,” Int. J. Comput. Appl., vol. 18, no. 6, pp. 40–46, 2011.
  12. P. S. Hiremath and J. Pujari, “Content Based Image Retrieval based on Color, Texture and Shape features using Image and its complement,” Int. J. Comput. Sci. Secur., vol. 1, no. 4, pp. 25–35, 2007.
  13. J. Li, J. Wang, and G. Wiederhold, “IRM: integrated region matching for image retrieval,” Proc. ACM Int. Conf. Multimed., pp. 147–156, 2000.
  14. D. Zhang, “Improving Image Retrieval Performance by Using Both Color and Texture Features,” Third Int. Conf. Image Graph. pp. 172–175, 2004.
  15. S. Neetu Sharma, S. Paresh Rawat, and S. Jaikaran Singh, “Efficient CBIR Using Color Histogram Processing,” Signal Image Process. An Int. J., vol. 2, no. 1, pp. 94–112, 2011.
  16. V. Kushwah and A. Agrawal, “Study on Query Based Clustering Technique for Content Based Image Retrieval,” IJREAT Int. J. Res. Eng. Adv. Technol., vol. 2, no. 1, pp. 1–6, 2014.
  17. Singha, M., & K.Hemachandran, “Content Based Image Retrieval using Color and Texture,” Signal Image Process. An Int. J., vol. 3, no. 1, pp. 39–57, 2012.
  18. Y. An, M. Riaz, and J. Park, “CBIR based on adaptive segmentation of HSV color space,” 12th Int. Conf. Comput. Model. Simul., pp. 248–251, 2010.
  19. R. Chakravarti and X. M. X. Meng, “A Study of Color Histogram Based Image Retrieval,” 2009 Sixth Int. Conf. Inf. Technol. New Gener., pp. 1323–1328, 2009.
  20. Institute of Electrical and Electronics Engineers (IEEE), “IEEE Standards Glossary of Image Processing and Pattern Recognition Terminology,” no. 610.4-1990, 1990.
  21. C. H. Wei, Y. Li, W. Y. Chau, and C. T. Li, “Trademark image retrieval using synthetic features for describing global shape and interior structure,” Pattern Recognit., vol. 42, no. 3, pp. 386–394, 2009.
  22. J. R. Smith and S.-F. Chang, “VisualSEEk: A Fully Automated Content-based Image Query System,” Proc. fourth ACM Int. Conf. Multimed. - Multimed. ’96, pp. 87–98, 1996.
  23. J. R. Smith and S.-F. C. S.-F. Chang, “Transform features for texture classification and discrimination in large image databases,” Proc. 1st Int. Conf. Image Process., vol. 3, pp. 407–411, 1994.
  24. D. A. Kumar and J. Esther, “Comparative Study on CBIR based by Color Histogram, Gabor and Wavelet Transform,” Int. J. Comput. Appl., vol. 17, no. 3, pp. 37–44, 2011.
  25. R. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3. pp. 610–621, 1973.
  26. L. Wang, Y. Zhang, and J. Feng, “On the Euclidean Distances of Images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1334–1339, 2005.
  27. J. Li and J. Z. Wang, “Automatic linguistic indexing of pictures by a statistical modeling approach,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, pp. 1075–1088, 2003.
  28. A. Chadha, S. Mallik, and R. Johar, “Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval,” Int. J. Comput. Appl., vol. 52, no. 20, pp. 35–42, 2012.
  29. B. S. Manjunath, J. R. Ohm, V. V. Vasudevan, and A. Yamada, “Color and texture descriptors,” IEEE Trans. Circuits Syst. Video Technol., vol. 11, no. 6, pp. 703–715, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

CBIR Color Histogram GLCM DWT Image Retrieval WANG image database Euclidean distance

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