CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Comparative Evaluation of Image Retrieval Algorithms using Relevance Feedback and it’s Applications

by Latika Pinjarkar, Kamal Mehta, Manisha Sharma, Kamal Mehta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 18
Year of Publication: 2012
Authors: Latika Pinjarkar, Kamal Mehta, Manisha Sharma, Kamal Mehta
10.5120/7447-0448

Latika Pinjarkar, Kamal Mehta, Manisha Sharma, Kamal Mehta . Comparative Evaluation of Image Retrieval Algorithms using Relevance Feedback and it’s Applications. International Journal of Computer Applications. 48, 18 ( June 2012), 12-16. DOI=10.5120/7447-0448

@article{ 10.5120/7447-0448,
author = { Latika Pinjarkar, Kamal Mehta, Manisha Sharma, Kamal Mehta },
title = { Comparative Evaluation of Image Retrieval Algorithms using Relevance Feedback and it’s Applications },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 18 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 12-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number18/7447-0448/ },
doi = { 10.5120/7447-0448 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:44:24.529640+05:30
%A Latika Pinjarkar
%A Kamal Mehta
%A Manisha Sharma
%A Kamal Mehta
%T Comparative Evaluation of Image Retrieval Algorithms using Relevance Feedback and it’s Applications
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 18
%P 12-16
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now adays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, Relevance Feedback (RF) techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. In this paper Content Based Image Retrieval algorithms using Relevance Feedback technique are discussed. The comparative study of these algorithms is done. This article covers various techniques for implementing Content Based Image Retrieval algorithms , their evaluation parameters used and various possible applications of Content Based Image Retrieval algorithms

References
  1. Ritendra Datta, Dhiraj Joshi, Jia Li, and James Z. Wang, "Image Retrieval: Ideas, Influences, and Trends of the New Age" , ACM Computing Surveys, Vol. 40, No. 2, Article 5, April 2008.
  2. Venkat N Gudivada "Relevance Feedback in Content-Based Image Retrieval"
  3. Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng "Fundamentals Of Content-Based Image Retrieval"
  4. Yong Rui, Thomas S. Huang, Michael Ortega, and Sharad Mehrotra " Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval" IEEE Trans. on Circuits and Systems For Video Technology, Vol. 8, No. 5, pp. 644-655,Sept 1998
  5. Pengyu Hong, Qi Tian, Thomas S. Huang "Incorporate Support Vector Machines To Content -Based Image Retrieval With Relevant Feedback" proceedings of IEEE International Conference on Image Processing (ICIP'2000), pp. 750-753, Vol. 3, Sep 10-13, 2000, Vancouver, Canada,
  6. Xiang Sean Zhou, Thomas S. Huang "Comparing Discriminating Transformations and SVM for Learning during Multimedia Retrieval" ACM Multimedia 2001, Ottawa, Canada.
  7. Zhong Su, Hongjiang Zhang, Stan Li, and Shaoping Ma "Relevance Feedback in Content-Based ImageRetrieval: Bayesian Framework, Feature Subspaces, and Progressive Learning" IEEE Trans. on Image processing, Vol. 12,No. 8,pp. 924-937, Aug 2003.
  8. Steven C. H. Hoi and Michael R. Lyu, Rong Jin "Integrating User Feedback Log into Relevance Feedback by Coupled SVM for Content-Based Image Retrieval"
  9. Wei Jiang, Guihua Er, Qionghai Dai, Jinwei Gu "Hidden annotation for image retrieval with long-term relevance feedback learning" Pattern Recognition Society. Published by Elsevier Ltd ,38 (2005) pp. 2007 – 2021
  10. Mohammed Lamine Kherfi and Djemel Ziou "Relevance Feedback for CBIR: A New Approach Based on Probabilistic Feature Weighting With Positive and Negative Examples" IEEE Trans. on Image Processing, Vol. . 15, No 4, April 2006
  11. Anelia Grigorova, Francesco G. B. De Natale, Charlie Dagli, andThomas S. Huang, "Content-Based Image Retrieval by Feature Adaptation and Relevance Feedback" IEEE Trans. on Multimedia, Vol. 9, No. 6, pp. 1183-1192 ,Oct. 2007.
  12. Wei Bian and Dacheng Tao, "Biased Discriminant Euclidean Embedding for Content-Based Image Retrieval" IEEE Trans. on Image Processing, Vol. 19, No. 2, pp. 545-554, Feb 2010
  13. Yu Sun, Bir Bhanu "Image Retrieval With Feature Selection and Relevance Feedback" Proceedings of IEEE 17th International Conference on Image Processing September 26-29, 2010, Hong Kong
  14. Ja-Hwung Su, Wei-Jyun Huang, Philip S. Yu, and Vincent S. Tseng " Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns" , IEEE Trans. On Knowledge and Data Engg. , Vol. 23, No 3,pp. 360-372 , March 2011
  15. Manish Chowdhury, Sudeb Das and Malay Kumar Kundu "Novel CBIR System Based on Ripplet Transform Using Interactive Neuro-Fuzzy Technique" Electronic Letters on Computer Vision and Image Analysis 11(1) pp:1-13, 2012
  16. S. Vaishnavi, Dr. T. T. Mirnalinee and Tina Esther Trueman "CBIR using Relevance Feedback Retrieval System" International Conference on Computing and Control Engineering (ICCCE 2012), Coimbatore Institute of Information Technology,12 & 13 April, 2012
  17. Shahrooz Nematipour, Jamshid Shanbehzadeh, Reza Askari Moghadam "Relevance Feedback Optimization in Content Based Image Retrieval Via Enhanced Radial Basis Function Network" Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol 1, (IMECS 2011), Hong Kong, March 16 - 18, 2011
  18. Xiang Sean Zhou , Thomas S. Huang "Relevance feedback in image retrieval: A comprehensive review" Proc. Springer-Verlag on Multimedia Systems, vol 8,pp. 536–544,2003
  19. Jing Xin and Jesse S. Jin " Relevance Feedback for Content-Based Image Retrieval Using Bayesian Network" Workshop on Visual Information Processing (VIP), Sydney,2000
Index Terms

Computer Science
Information Sciences

Keywords

Content-based Image Retrieval Relevance Feedback Precision Convergence

Powered by PhDFocusTM