CFP last date
20 June 2024
Reseach Article

Combination of Local, Global and K-Mean using Wavelet Transform for Content Base Image Retrieval

by Ekta Gupta, Rajendra Singh Kushwah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 14
Year of Publication: 2015
Authors: Ekta Gupta, Rajendra Singh Kushwah

Ekta Gupta, Rajendra Singh Kushwah . Combination of Local, Global and K-Mean using Wavelet Transform for Content Base Image Retrieval. International Journal of Computer Applications. 116, 14 ( April 2015), 5-9. DOI=10.5120/20402-2739

@article{ 10.5120/20402-2739,
author = { Ekta Gupta, Rajendra Singh Kushwah },
title = { Combination of Local, Global and K-Mean using Wavelet Transform for Content Base Image Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 14 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { },
doi = { 10.5120/20402-2739 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:57:05.727683+05:30
%A Ekta Gupta
%A Rajendra Singh Kushwah
%T Combination of Local, Global and K-Mean using Wavelet Transform for Content Base Image Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 14
%P 5-9
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

With the ever expanding database and advancement of technology in the fields of Data mining, remote sensing and management of Earth resources, Crime prevention, Weather Forecasting, E-commerce, Medical Imaging, and soon. The Content Based Image Retrieval Technique is becoming more and more indispensable and vital. The paper proposes Content Based Image Retrieval technique incorporating WBCHIR (Wavelet Based Color Histogram Image Retrieval) which utilizes features of an image like Color and Texture. The shape and shade features are extracted in the course of Wavelet Transform and Color Histogram, and the arrangement of these features is the vital the scaling and conversion of objects into an image. Now, it is being presented for the first time in our era that techniques such as Feature Extraction, segmentation and Grid, K-means module and k-nearest neighborhood module are integrated together to build the CBIR System. It is a hybrid of Global and Local Features method with K-means Clustering algorithm. Given a set of instruction images, a K-means Clustering Algorithm is applied to cluster the regions on the basis of these features. These features, which they identify as "Blobs", compose the expressions for the set of images. Each of these "blobs" is assigned an exclusive integer to serve as its identifier (analogous to a word's ASCII representation. In this paper, we present a technique for integration of Wavelet Based Color Histogram Image Retrieval (WBCHIR) using color and texture features into Content Based Image Retrieval. The Evaluation between the images is ascertained by means of a Distance Function. The concept proposed in this paper will provide better results as compared to other retrieval methods in terms of average accuracy. Moreover, the computational steps are summarily consistent with the use of Wavelet transformation.

  1. J. Philbin, O. Chum, M. Isard, J. Sivic, A. Zisserman, Object retrieval with large vocabularies and fast spatial matching, in: IEEE Con- faience on Computer Vision and Pattern Recognition, 2007 (CVPR'07), IEEE, 2007, pp-1–8.
  2. J. Yu, D. Liu, D. Tao, H. Seah, Complex object correspondence construction in two-dimensional animation, IEEE Transactions on Image Processing 11 (2012) pp. 3257–3269.
  3. M. Subrahmanyam,R. Maheshwari,R. Balasubramanian, Local maximum edge binary patterns: a new descriptor for image retrieval and object tracking, Signal Processing 92 (6) (2012) pp-1467–1479.
  4. X. Tian, D. Tao, X. Hua, X. Wu, Active reranking for web image search, IEEE Transactions on Image Processing 19 (3) (2010) pp-805–820.
  5. Tian, X. ,Tao D. and Rui, Y. 2011. Sparse Transfer Learning for Interactive Video Search Reranking. ACM Trans. Multimedia Computing, Communications and Applications.
  6. C. Manning, P. Raghavan, H. Schutze, Introduction to Information Retrieval, Cambridge University Press, Cambridge, 2008. Pp- 256-280.
  7. B. Geng, L. Yang, C. Xu, A study of language model for image retrieval, in: IEEE International Conference on Data Mining Work- shops, 2009 (ICDMW'09), IEEE, 2009, pp-158–163.
  8. B. Geng, L. Yang, C. Xu, X. Hua, Ranking model adaptation for domain- specific search, in: Proceedings of the 18th ACM Conference on Information and Knowledge Management, ACM, 2009, pp-197–206.
  9. M. Datar, N. Immorlica, P. Indyk, V. Mirrokni, Locality-sensitive hashing scheme based on p-stable distributions, in: Proceedings of the 20th Annual Symposium on Computational Geometry, ACM, 2004, pp-253–262.
  10. Y. Li, B. Geng, Z. Zha, D. Tao, L. Yang, C. Xu, Difficulty guided image retrieval using linear multiview embedding, in: Proceedings of the 19th ACM International Conference on Multimedia, ACM, 2011, pp-1169–1172.
  11. Y. Jing, S. Baluja, Pagerank for product image search, in: Proceeding of the 17th International Conference on World Wide Web, ACM, 2008, pp-307–316.
  12. C. H. Lin, R. T. Chen and Y. K. Chan, "A smart content-based image retrieval system based on color and texture feature", Image and Vision Computing vol. 27, pp-658–665, 2009.
  13. N. Jhanwar, S. Chaudhurib, G. Seetharamanc and B. Zavidovique, "Content based image retrieval using motif co-occurrence matrix", Image and Vision Computing, Vol. 22, pp-1211–1220, 2004.
  14. P. W. Huang and S. K. Dai, "Image retrieval by texture similarity", Pattern Recognition, Vol. 36, pp- 665–679, 2003.
  15. P. S. Hiremath and J. Pujari, "Content Based Image Retrieval based on Color, Texture and Shape features using Image and its complement", 15th International Conference on Advance Computing and Communications. IEEE. 2007. pp- 262-282.
  16. Y. Chen and J. Z. Wang, "A Region-Based Fuzzy Feature Matching Approach to Content Based Image Retrieval", IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 24, No. 9, pp-1252-1267, 2002.
  17. Y. Rubner, L. J. Guibas and C. Tomasi, "The earth mover's distance, multidimensional scaling, and color-based image retrieval" , Proceedings of DARPA Image understanding Workshop. pp-661-668, 1997.
  18. M. B. Rao, B. P. Rao, and A. Govardhan, "CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features", International Journal of Computer Applications, Vol. 18– No. 6, pp-0975-8887, 2011.
  19. A. Natsev, R. Rastogi and K. Shim, "WALRUS: A SimilarityRetrieval Algorithm for Image Databases", In Proceeding. ACMSIGMOD Int. Conf. Management of Data, pp-395–406, 1999.
  20. R. C. Gonzalez, R. E. Woods and S. L, Eddins. Digital Image Processing Using MALAB, By Pearson Education, 2008. pp-426-437.
  21. R. M. Haralick, K. Shanmugam, I. Dinstein, "Textural Features for Image Classification", IEEE Transactions on Systems, Man, and Cybernetics, pp. 610-621, 1973.
  22. Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Pearson Education, Third Edition, Copyright © 2008. pp-372-378.
  23. Structured representations in a content based image retrieval context RomainRaveaux, Jean-Christophe Burie , Jean-Marc Ogier. pp-1252-1268
  24. Fusion of Local and Global Features using Stationary Wavelet Transform for Efficient Content Based Image Retrieval 2014 IEEE Students' Conference on Electrical, Electronics and Computer Science.
Index Terms

Computer Science
Information Sciences


CBIR K-means DWT Global Feature Local Feature.