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Web Image Retrieval using Clustering Approaches

Published on None 2011 by Umesh K K, Suresha
journal_cover_thumbnail
International Symposium on Devices MEMS, Intelligent Systems & Communication
Foundation of Computer Science USA
ISDMISC - Number 6
None 2011
Authors: Umesh K K, Suresha
222f3778-32f0-411f-93f5-e633d65efafe

Umesh K K, Suresha . Web Image Retrieval using Clustering Approaches. International Symposium on Devices MEMS, Intelligent Systems & Communication. ISDMISC, 6 (None 2011), 30-35.

@article{
author = { Umesh K K, Suresha },
title = { Web Image Retrieval using Clustering Approaches },
journal = { International Symposium on Devices MEMS, Intelligent Systems & Communication },
issue_date = { None 2011 },
volume = { ISDMISC },
number = { 6 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 30-35 },
numpages = 6,
url = { /proceedings/isdmisc/number6/3482-isdm141/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Symposium on Devices MEMS, Intelligent Systems & Communication
%A Umesh K K
%A Suresha
%T Web Image Retrieval using Clustering Approaches
%J International Symposium on Devices MEMS, Intelligent Systems & Communication
%@ 0975-8887
%V ISDMISC
%N 6
%P 30-35
%D 2011
%I International Journal of Computer Applications
Abstract

In this paper, we propose an improved region-based image retrieval system. The system applies image segmentation to divide an image into discrete regions, which helps to correspond to objects. The main discussion of this paper is to compute the signature of an input image at the browser and sends this signature to the database to retrieve the similar images with set of URLs. The signature of web images is stored in the database in the each object to indexing and retrieval performance and also to provide a better similarity distance computation. In addition, similarities distance computation we introduced object weight based on object’s uniqueness. Therefore, objects that are not unique such as trees and skies will have less weight. The experimental evaluation is based on the IRM and Geometric Histogram and the performance is compared between them. As compared with existing technique and systems, such as IRM and Geometric Histogram, our study demonstrate the following unique advantages: (i) an improvement in image segmentation accuracy using the modified k-means algorithm (ii) an improvement in retrieval accuracy as a result of a better similarity distance computation that considers the importance and uniqueness of objects in an image.

References
  1. Michael J. Swain., Charles Frankel., and Vassilis Athitsos, “WebSeer: An Image Search Engine for the World Wide Web”, Technical Report 96-14, 1997.
  2. J. R. Smith, “Integrated Spatial and Feature Image Systems: Retrieval, Compression and Analysis”. PhD thesis, Graduate School of Arts and Sciences, Columbia University, February 1997.
  3. S. Sclaroff., L. Taycher., and M. La Cascia. “Imagerover: A content-based image browser for the world wide Web”. In Proceedings IEEE Workshop on Content-based Access of Image and Video Libraries, June ’97, 1997.
  4. Michael Ortega., Yong Rui., Kaushik Chakrabarti., Sharad Mehrotra., and Thomas S. Huang. “Supporting similarity queries in MARS”. In Proc. Of ACM Conf. on Multimedia, 1997.
  5. R. Baeza-Yates and C. Castillo, “quality and freshness in Web crawling”, In Soft 870 Computing Systems - Design, Management and Applications, pages 565- 572, IOS Press Amsterdam, Santiago, Chile, 2002.
  6. Li, J., Wang, J. Z. and Wiederhold, G., (2000), “Integrated Region Matching for Image Retrieval,” ACM Multimedia, p. 147-156.
  7. Zhang, R. and Zhang, Z., (2002), “A Clustering Based Approach to Efficient Image Retrieval”, Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence, pp. 339.
  8. Daubechies, I., (1992), “Ten Lectures on Wavelets,” Capital City Press, Montpelier,Vermont.
  9. Guha, S., Rastogi, R., and Shim, K., (1998), “CURE: An Efficient Clustering Algorithm for Large Databases” Proc. of ACM SIGMOD International Conference on Management of Data, pp.73-84.
  10. Seo, J., Bakay, M., Zhao, P., and Chen Y., Clarkson P., Shneiderman, B., Hoffman E. P.,(2003), “Interactive Color Mosaic and Dendrogram Displays for Signal/Noise Optimization in Microarray Data Analysis” IEEE International Conference on Multimedia and Expo.
  11. Pentland, A., Picard, R., and Sclaroff S.,(1996), “Photobook: Content based manipulation of image databases”, International Journal of Computer Vision, 18(3), pp.233–254.
  12. Smith, J.R., and Chang, S.F., (1997), “Single color extraction and image query,” InProceeding IEEE International Conference on Image Processing, pp. 528–531.
  13. Gupta, A., and Jain, R., (1997), “Visual information retrieval” Comm. Assoc. Comp. Mach., 40(5), pp. 70– 79.
  14. Shi, J., and Malik, J., “Normalized Cuts and Image Segmentation,” Proceedings Computer Vision and Pattern Recognition, June, 1997, pp. 731-737.
  15. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Qian Huang, Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., and Yanker, P., IBM Almaden Res. Center, San Jose, CA.
  16. Umesh, K. K., and Suresha., “Region based Color histogram Features for Efficient Web Image Retrieval” , Journal of Computing, vol. 2, Issue 9, September 2010, ISSN 2151-9617.
Index Terms

Computer Science
Information Sciences

Keywords

Region based image retrieval hierarchical clustering image classification image segmentation region matching