CFP last date
22 July 2024
Reseach Article

A New Relevance Feedback based Approach for Efficient Image Retrieval

by Karthik S, Snehanshu Saha, Chaithra G
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 14
Year of Publication: 2013
Authors: Karthik S, Snehanshu Saha, Chaithra G

Karthik S, Snehanshu Saha, Chaithra G . A New Relevance Feedback based Approach for Efficient Image Retrieval. International Journal of Computer Applications. 61, 14 ( January 2013), 1-6. DOI=10.5120/9993-4846

@article{ 10.5120/9993-4846,
author = { Karthik S, Snehanshu Saha, Chaithra G },
title = { A New Relevance Feedback based Approach for Efficient Image Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 14 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { },
doi = { 10.5120/9993-4846 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T21:09:04.642413+05:30
%A Karthik S
%A Snehanshu Saha
%A Chaithra G
%T A New Relevance Feedback based Approach for Efficient Image Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 14
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA

The rapid growth of digital image data summons the need for an effective and efficient content-based image searching system. Such systems should address the needs of the end user and should deliver the relevant images based on the search criteria. In order to meet this requirement, the content-based image search technique should capture the color and texture information. The performance of the algorithm can be enhanced using relevance feedback method. In this paper, a content-based image retrieval method based on image and its complement is presented. The similarity between the images is identified using an approach based on most significant highest priority (MSHP) principle or using a new distance measure which belongs to minkowski family. The retrieval rate is enhanced by relevance feedback technique based on k-means algorithm. The approach is tested on Simplicity test dataset and a comparable performance was achieved

  1. P Kshirsagar, V Munde, S Deshpande, " Semantic Based Search technology for Images", International conference and workshop on emerging trends in Technology, Maharastra, Feb 26-27, 2010, pp 550-553
  2. Fabio F Faria, Adriano Veloso, Humberto Almeida, Eduardo Valle, Ricardo S Torres, Marcos A Goncalves, " Learning to Rank for Content Based Image Retrieval", MIR-2010, Pennsylvania, March 29-31, 2010, pp 285-294
  3. Giorgio Giacinto, " A Nearest Neighbor Approach to Relevance Feedback in Content based image retrieval", CIVR-2007, July 9-11, 2007, pp 456-463
  4. Wei Bian, Dacheng Tao, " Biased Discriminant Euclidean Embedding for Content based image retrieval", IEEE transactions on image processing, Vol 19, No 2, February 2010, pp 545-554
  5. Kien A Hua, Khanh Vu, Jung-Hwan Oh, " SamMatch: A flexible and efficient sampling based image retrieval technique for large image databases", ACM transaction on Multimedia, 1999, pp 225-234
  6. Juan C Caicedo, Fabio A G, Edwin Triana, Eduardo Romero, " Design of a Medical image database with content based retrieval capabilities", PSIVT 2007, pp 919-931
  7. Mei-Ling Shyu, Shu-Ching Chen, Chengcui Zhang, " A unified framework for image database clustering and content based retrieval", MMDB-04, November 13,2004, pp 19-27
  8. Feng Jing, Bo Zhang, Fuzong Lin, Wei-Ying ma, " A novel region based image retrieval method using relevance feedback", International conference on Multimedia information retrieval, 2001, pp 28-31
  9. P. S. Hiremath, Jagadeesh Pujari, "Content Based Image Retrieval based on Color, Texture and Shape features using Image and its complement", International Journal of Computer Science and Security, Volume 1, Issue 4, 2007,pp. 25-35
  10. Y. Rubner, L. J. Guibas, and C. Tomasi, "The earth mover's distance, multi-dimensional scaling, and color-based image retrieval", Proceedings of DARPA Image understanding Workshop, 1997, pp. 661-668
  11. J. Li, J. Z. Wang, and G. Wiederhold, "IRM: Integrated Region Matching for Image Retrieval," Proc. of the 8th ACM International Conference on Multimedia, 2000, pp. 147-156.
  12. M. Banerjee, M,K,Kundu and P. K. Das, "Image Retrieval with Visually Prominent Features using Fuzzy set theoretic Evaluation", ICVGIP, 2004.
  13. Courant,R, D. Hilbert, " Methods of Mathematical Physics", Interscience/Wiley publication, 1989
  14. Erwin Kreyszig, " Introductory Functional Analysis with applications", Wiley publications, 1978
Index Terms

Computer Science
Information Sciences


Content based image retrieval relevance feedback K-means algorithm Image search Most Significant Highest Priority Simplicity dataset