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Reseach Article

Color - Texture based Image Retrieval System

by Rahul Mehta, Nishchol Mishra, Sanjeev Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 24 - Number 5
Year of Publication: 2011
Authors: Rahul Mehta, Nishchol Mishra, Sanjeev Sharma
10.5120/2958-3910

Rahul Mehta, Nishchol Mishra, Sanjeev Sharma . Color - Texture based Image Retrieval System. International Journal of Computer Applications. 24, 5 ( June 2011), 24-29. DOI=10.5120/2958-3910

@article{ 10.5120/2958-3910,
author = { Rahul Mehta, Nishchol Mishra, Sanjeev Sharma },
title = { Color - Texture based Image Retrieval System },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 24 },
number = { 5 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume24/number5/2958-3910/ },
doi = { 10.5120/2958-3910 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:12.034972+05:30
%A Rahul Mehta
%A Nishchol Mishra
%A Sanjeev Sharma
%T Color - Texture based Image Retrieval System
%J International Journal of Computer Applications
%@ 0975-8887
%V 24
%N 5
%P 24-29
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content Based Image Retrieval (CBIR) is an interesting and most emerging field in the area of ‘Image Search’, in which similar images for the given query image searched from the image database. Current systems use color, texture and shape information for image retrieval. In this paper wepropose a method in which both color and texturefeatures of the images are used to improve the retrieval results in terms of its accuracy. Color extraction and comparison are performed using Conventionalcolor histograms (CCH) and the Quadratic Distance Metric (QDM)and the texture extraction and comparison are performed using the concept ofPyramid Structure Wavelet Transform Model (PSWTM) and the Euclidean distance. Color and texture based image retrieval computes image features more accurately whichare used to retrieve similar images from the database.

References
  1. Xiang-Yang Wang, Jun-Feng Wu1 and Hong-Ying Yang "Robust image retrieval based on color histogram of local feature regions" Springer Netherlands, 2009 ISSN 1573-7721.
  2. D.Goswami, S.K.Bhatia, “RISE: A Robust Image Search Engine”, Electro/information Technology, 2006 IEEE International Conference on, 7-10 May 2006, pp. 354-359.
  3. A.Ahmadian, A.Mostafa, M.D.Abolhassani, Y.Salim- pour, “A texture classification method for diffused liver diseases using Gabor wavelets”, Engineering in Medicine and Biology Society, 2005.
  4. N. Jhanwar, S. Chaudhurib, G. Seetharamanc, B. Zavidovique, Content based image retrieval using motif co-occurrence matrix, Image and Vision Computing 22 (2004) 1211–1220.
  5. P. W. Huang and S. K. Dai, “Design of a two-stage content-based image retrieval system using texture similarity,” Information Processing and Management, vol. 40, no.1,pp.81–96,2004.
  6. S.K.Saha, A.K.Das, B.Chanda, “CBIR using Perception based Texture and Color Measures”, Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, 23-26 Aug.2004,pp.985-988Vol.2.
  7. P.W. Huang, S.K. Dai, Image retrieval by texture similarity, Pattern Recognition36 (3) (2003)665–679.
  8. J. Han and K.-K. Ma, “Fuzzy color histogram and its use in color image retrieval,” IEEE Transactions on Image Processing, vol. 11, no. 8, pp. 944–952, 2002.
  9. SharminSiddique, “A Wavelet Based Technique for Analysis and Classification of Texture Images,” Carleton University, Ottawa, Canada, Project Report 70.593, April 2002.
  10. Shengjiu Wang, “A Robust CBIR Approach Using Local Color Histograms,” Department of Computer Science, University of Alberta, Edmonton, Alberta, Canada, October 2001, Found at: http://citeseer.nj.nec.com/wang01robust.html
  11. W. K. Pratt, Digital Image Processing, Wiley-Interscience, New York, NY, USA, 3rd edition, 2001.
  12. I. Daubechies, Orthonormal bases of compactly supported wavelets,Communications on Pure and Applied Mathematics 41 (1998) 909–996.
  13. W. Niblack, X. Zhu, J. L. Hafner, et al., “Updates to the QBIC system,” in Storage and Retrieval for Image and Video Databases VI, vol. 3312 of Proceedings of SPIE, pp. 150–161, San Jose, Calif, USA, January 1998.
  14. F. Liu and R. W. Picard, “Periodicity, directionality, and randomness: wold features for image modeling and retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 7, pp. 722–733, 1996.
  15. J. R. Smith and S. F. Chang, “Automated image retrieval using color and texture,” Tech. Rep. CU/CTR 408-95-14, Columbia University, New York, NY, USA, July 1995.
  16. W. Niblack et al., “Querying images by content, using color, texture, and shape”, SPIE Conference on Storage and Retrieval for Image and Video Database, Volume 1908,pp.173-187, April 1993.
  17. W. Niblack, R. Barber, W. Equitz, et al., “The QBIC project: querying images by content, using color, texture, and shape,” in Storage and Retrieval for Image and Video Databases II, vol. 1908 of Proceedings of SPIE, pp. 173–187, 1993.
  18. R.M. Haralick, L.G. Shapiro, Computer and Robot Vision, vol. I, Addison-Wesley,Reading, MA, 1992.
  19. M.J. Swain, D.H. Ballard, Color Indexing, International Journal of Computer Vision 7 (1991) 11–32.
Index Terms

Computer Science
Information Sciences

Keywords

CBIR Color based Search Texture based Searching Color Histogram Pyramid Structure Wavelet Transform model Euclidean Distance Quadratic Distance Metric