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A Efficient Content based Image Retrieval System using GMM and Relevance Feedback

by Ramadass Sudhir, S. Santhosh Baboo
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 22
Year of Publication: 2013
Authors: Ramadass Sudhir, S. Santhosh Baboo
10.5120/12678-9425

Ramadass Sudhir, S. Santhosh Baboo . A Efficient Content based Image Retrieval System using GMM and Relevance Feedback. International Journal of Computer Applications. 72, 22 ( June 2013), 50-61. DOI=10.5120/12678-9425

@article{ 10.5120/12678-9425,
author = { Ramadass Sudhir, S. Santhosh Baboo },
title = { A Efficient Content based Image Retrieval System using GMM and Relevance Feedback },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 22 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 50-61 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number22/12678-9425/ },
doi = { 10.5120/12678-9425 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:38:39.941616+05:30
%A Ramadass Sudhir
%A S. Santhosh Baboo
%T A Efficient Content based Image Retrieval System using GMM and Relevance Feedback
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 22
%P 50-61
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content-Based Image Retrieval (CBIR) systems are required to effectively extract information from ubiquitous image collections. Retrieving images from a large and highly varied image data set based on their visual contents is extremely challenging. CBIR has been studied for decades and many good approaches have been proposed. But they do have some drawbacks. Texture and color are the significant features of CBIR systems. This paper gives a novel method of CBIR, in which images can be retrieved using color-based, texture-based and color and texture-based. Auto color correlogram and correlation for extracting color based images, Gaussian mixture models for extracting texture based images are the algorithms used here. For Relevance Feedback, Query Point Movement technique is used. Thus the proposed method achieves better performance and accuracy in retrieved images along with iteration reduction.

References
  1. Ot´avio A. B. Penatti, Eduardo Valle, Ricardo da S. Torres, "Comparative Study of Global Color and Texture Descriptors for Web Image Retrieval, Journal of Visual Communication and Image RepresentationSeptember 16, 2011
  2. V. Gudivada and V. Raghavan. Content-based image retrieval systems. IEEE Computer, 28(9):18 – 22, September 1995.
  3. Pushpa B. Patil, Manesh B. Kokare, " Relevance Feedback in Content Based Image Retrieval: A Review," Journal of Applied Computer Science & Mathematics, no. 10 (5) /2011, Suceava
  4. Samantha K. Hastings. Evaluation of image retrieval systems: Role of user feedback Library Trends, 48(2):422 – 436, 1999.
  5. Rui, Y. , Huang, T. S. , and Mehrotra,S. "Content-based Image Retrieval with Relevance Feedback in MARS", in Proc. IEEE Int. Conf. on Image proc. , 1997
  6. Rui, Y. ; Huang, T. ; Ortega, M. ; Mehrotra,S. "Relevance Feedback : A Power Tool In Interactive Content-Based Image Retrieval," IEEE Transactions on Circuits and Systems for Video Technology , Vol. 8(5), pp. 644-655, 1998.
  7. Monika Jain, S. K. Singh, "A Survey On: Content Based Image Retrieval Systems Using Clustering Techniques For Large Data sets", International Journal of Managing Information Technology (IJMIT) vol. 3, no. 4, November 2011.
  8. Michelle Chang et al. Collection understanding. In JCDL '04: Proceedings of the 4th ACM/IEEE-CS Joint conference on Digital libraries, pages 334–342, New York, NY, USA, 2004. ACM Press. ISBN: 1-58113-832-6.
  9. G. Pass, and R. Zabith, "Comparing images using joint histograms," Multimedia Systems, vol. 7, pp. 234-240, 1999.
  10. Ianus Keller, Pieter Jan Stappers, and Sander Vroegindeweij. Supporting informal collection of digital images: organizing, browsing and sharing. In Proceedings of the conference of Dutch directions in HCI, page 17, New York, NY, USA, 2004. ACM Press. ISBN: 1-58113- 944-6. .
  11. J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using crossmedia relevance models. In SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 119–126, New York, NY, USA, 2003.
  12. Venkat N Gudivada, "Relevance Feedback in Content-Based Image Retrieval", International Journal of Computer Science and Engineering, vol. 3,no. 1, 2010.
  13. Alberto Del Bimbo. Visual Information Retrieval. Morgan Kaufmann Publishers, Inc. , 2001.
  14. Jing Xin and Jesse S. Jin "Relevance Feedback for Content-Based Image Retrieval Using Bayesian Network" Workshop on Visual Information Processing (VIP), Sydney, 2003.
  15. Pushpa B. Patil, Manesh B. Kokare, "Relevance Feedback in Content Based Image Retrieval: A Review", Journal of Applied Computer Science & Mathematics, vol. 5, no. 10 ,2011.
  16. Samuel Rota Bulo, Massimo Rabbi and Marcello Pelillo, "Content-Based Image Retrieval with Relevance Feedback using Random Walks", Pattern Recognition, June 6, 2011.
  17. https://sites. google. com/site/dctresearch/Home/content-based-image-retrieval from COREL image dataset.
Index Terms

Computer Science
Information Sciences

Keywords

Image retrieval Texture Auto Color Correlogram (ACC) Gaussian Mixture Models (GMM) Query Point Movement Content-Based Image Retrieval (CBIR)

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