CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

UNIQUE CBIR – A Unified Method of Retrieving Similar Images

by G. UmaMaheswar, P. Ramana Reddy
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 18
Year of Publication: 2015
Authors: G. UmaMaheswar, P. Ramana Reddy
10.5120/ijca2015906738

G. UmaMaheswar, P. Ramana Reddy . UNIQUE CBIR – A Unified Method of Retrieving Similar Images. International Journal of Computer Applications. 127, 18 ( October 2015), 35-39. DOI=10.5120/ijca2015906738

@article{ 10.5120/ijca2015906738,
author = { G. UmaMaheswar, P. Ramana Reddy },
title = { UNIQUE CBIR – A Unified Method of Retrieving Similar Images },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 18 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number18/22833-2015906738/ },
doi = { 10.5120/ijca2015906738 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:18:35.175875+05:30
%A G. UmaMaheswar
%A P. Ramana Reddy
%T UNIQUE CBIR – A Unified Method of Retrieving Similar Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 18
%P 35-39
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content Based Image Retrieval (CBIR) is a method of extracting image features and calculate visual similarity based on these features. Lot of research activity is continuing for optimizing and improving the feature extraction methods focusing on colour, texture and shape features to minimize the semantic gap. Relevant Feedback (RF) is one variant of CBIR wherein the user provides feedback by selecting the most relevant image from retrieved images and make it a query image for the subsequent iterations. The RF methods improved the performance of the overall system in multiple iterations but the time taken for the total process increases on the other hand. This paper attempts to address the semantic gap problem by having the user interface in the forward path by adding some more key requirements of the user for narrowing down the search options thereby increasing the system performance and reducing the retrieval time.

References
  1. Fundamentals of Content Based image Retrieval by Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng
  2. Ritendra Datta Jia Li James Z. Wang , Content-Based Image Retrieval - Approaches and Trends of the new age, ACM 1-58113-940 , MIR’05, November 11-12, Singapore, 2005.
  3. B.L. Deekshatulu, Learning Semantics in Content Based Image Retrieval (CBIR) – A brief review. 978-0-7695-4326 IEEE Computer Society , 2010
  4. Ritendra Datta , Dhiraj Joshi , Jia Li, and James Z. Wang , Image Retrieval: Ideas, Influences, and Trends of the New Age, ACM Transactions on Computing Surveys, Vol. 40, No. 2, April 2008
  5. Yong Rui, Thomas S. Huang, Michael Ortega and Sharad Mehrotra, Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval,, IEEE Transactions on circuits and video technology , 1998
  6. Darshak Thakur,A.I Trivedi, Content based image retrieval techniques – Issues, analysis and the state of the art , Proceedings ICACCI 2014, New Delhi (2014)
  7. Arnold.W.M.Smeulders,Simoneantini,Amarnath Gupta,Ramesh Jain: Content Based Image Retrieval at the end of the early years, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, No.12, Dec 2000
  8. Fumihiho Kawanobe, Shigeru Takano, and Yoshihiro Okada : Towards interactive image query system for content-based image retrieval, Fourth International Workshop on Semantic Media Adaptation and Personalization, 978-0-7695-3894-5/09 (2009)
  9. Pushpa B. Patil, Manesh B. Kokare,: Interactive Content-Based Texture Image Retrieval, International Conference on Computer & Communication Technology (ICCCT)- (2011)
  10. Thomas Deselaers Æ Daniel Keysers Hermann Ney, Features for image retrieval: an experimental comparison Inf Retrieval (2008) 11:77–107, Springer Science Business Media, LLC 2007 (2008)
  11. Hui Yu, Mingjing Li, Hong-Jiang Zhang, Juh Feng, “Color Texture Moments For Content-Based Image Retrieval”, 0-7803-7622-6/02/ - 2002- IEEE
  12. Jana Zujovic, Thrasyvoulos N. Pappas , David L. Neuhoff , Rene van Egmond , Huib Ridder : A New Subjective Procedure for Evaluation and development of Texture Similarity Metrics, IVMSP 2011
  13. Karin Kailing, Hans-Peter Kriegel, and Stefan Sch¨onauer; Content-Based Image Retrieval Using Multiple Representations, Proc. 8th Int. Conf. on Knowledge-Based Intelligent Information and Engineering Systems (KES'04), Wellington, New Zealand, LNAI 3214, pp. 982-988, 200
  14. Gita Das and Sid Ray: A Comparison of Relevance Feedback Strategies in CBIR, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007), 0-7695-2841-4/07( 2007)
  15. Rajdeep Kaur, Kamaljit Kaur, Study of Different Techniques for Image Retrieval , International Journal of Advanced Research in Computer Science and Software Engineering., Volume 5, Issue 4 , 2015
Index Terms

Computer Science
Information Sciences

Keywords

Content Based Image Retrieval - Relevant Feedback – Similarity - Feature Vector

Powered by PhDFocusTM