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Survey on Content based Image Retrieval to Deal with Rapid Growth of Digital Images

by Nitish Barya, Himanshu Jaiswal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 12
Year of Publication: 2015
Authors: Nitish Barya, Himanshu Jaiswal
10.5120/ijca2015905692

Nitish Barya, Himanshu Jaiswal . Survey on Content based Image Retrieval to Deal with Rapid Growth of Digital Images. International Journal of Computer Applications. 124, 12 ( August 2015), 29-32. DOI=10.5120/ijca2015905692

@article{ 10.5120/ijca2015905692,
author = { Nitish Barya, Himanshu Jaiswal },
title = { Survey on Content based Image Retrieval to Deal with Rapid Growth of Digital Images },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 12 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number12/22158-2015905692/ },
doi = { 10.5120/ijca2015905692 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:14:14.383306+05:30
%A Nitish Barya
%A Himanshu Jaiswal
%T Survey on Content based Image Retrieval to Deal with Rapid Growth of Digital Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 12
%P 29-32
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Development in image retrieval systems has increased in large part due to the rapid growth of the digital images produced by World Wide Web and high capacity of digital data storage devices available in the human race domain. A desired image from network and storage media is shared by citizens belonging to various field, including education, business, government agencies, journalism, and advertising agencies. But due to generation of large collection of digital images, users are not satisfied with the traditional information retrieval techniques. As the elaboration of multimedia technologies are becoming more trendy, so these days the content based image retrieval are becoming a foundation of exact and fast retrieval. This survey paper deals with the techniques of content based image retrieval using both local features and global features. One of them is conventional color histogram and fuzzy color histogram. Further Support vector machine (SVM) with optimized feature sub set selection using radial bias network can also be used to improve retrieval performance.

References
  1. R.Joe Stanleya Soumya Dea, Dina Demner-Fushmanb, Sameer Antanib, George R. Thomab “An image feature-based approach to automatically find images for application to clinical decision support”. Computer Med Imaging Graph. 2011 Jul; 35 (5):365-72.
  2. Hiremath P.S. and Jagadeesh Pujari “Content Based Image Retrieval using Color Boosted Salient Points and Shape features of an image” International Journal of Image Processing, Volume (2) : Issue (1).
  3. Heng Chen and Zhicheng Zhao “An effective relevance feedback algorithm for image retrival”inIEEE 2012.This paper appears in:Network Infrastructure and Digital Content (IC-NIDC), 2012 3rd IEEE International Conference.
  4. Dr. H. B. Kekre, Dhirendra Mishra “CBIR using Upper Six FFT Sectors of Color Images for Feature Vector Generation” H.B.Kekre. et al /International Journal of Engineering and Technology Vol.2(2), 2010, 49-54.
  5. Jalil Abbas, Salman Qadri, Muhammad Idrees, Sarfraz Awan, Naeem Akhtar Khan1 “Frame Work for Content Based Image Retrieval (Textual Based) System” Journal of American Science 2010; 6(9).
  6. Ryszard S. Chora´s “Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems” international journal of biology and biomedical engineering, 2007.
  7. Hichem Bannour_Lobna Hlaoua_Bechir Ayeb, “Survey of the Adequate Descriptor for Content Based Image Retrieval on the Web: Global Versus Local Features “2009.
  8. Ch.Srinivasa rao , S. Srinivas kumar #, B.N.Chatterji “ Content Based Image Retrieval using Contourlet Transform” ICGST-GVIP Journal, Volume 7, Issue 3, November 2007
  9. Dr. H. B. Kekre, Dhirendra Mishra “CBIR using Upper Six FFT Sectors of Color Images for Feature Vector Generation” H.B.Kekre. et al /International Journal of Engineering and Technology Vol.2(2), 2010, 49-54.
  10. Zhe-Ming Lu, Su-Zhi Li and Hans Burkhardt , “ A Content-Based Image Retrieval Scheme in Jpeg Compressed Domain ” International Journal of Innovative Computing, Information and Control ICIC International °c 2006 ISSN 1349-4198 Volume 2, Number 4, August 2006.
  11. Ramesh Babu Durai C “A Generic Approach To Content Based Image Retrieval Using Dct And Classification Techniques” (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 06, 2010, 2022-2024.
  12. S. Nandagopalan, Dr. B. S. Adiga, and N. Deepak “A Universal Model for Content-Based Image Retrieval” World Academy of Science, Engineering and Technology 46 2008.
  13. D. N. F. AwangIskandar James A. Thom S. M. M. Tahaghoghi “Content-based ImageRetrieval Using Image Regions as Query Examples” 9th conference on Australasian database, Volume 75, Pages 38-46.
  14. Issam El-Naqa, Yongyi Yang , Nikolas P. Galatsanos , Robert M. Nishikawa , and Miles N. Wernick , “A Similarity Learning Approach to Content-Based Image Retrieval: Application to Digital Mammography ” Ieee Transactions On Medical Imaging, Vol. 23, No. 10, October 2004
  15. Chih-Chin Lai, Member, IEEE, and Ying-Chuan Chen “A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm”. IEEE Transaction on Instrumentation and Measurement, Volume 60, Issue 10, Pages 3318-3325.
  16. Ming Qi, Guangzhong Sun and GuoliangChen“Parallel and SIMD Optimization of Image Feature Extraction” International Conference on Computational Science, ICCS 2011.
  17. M. Subrahmanyam, R.P. Maheshwari and R. Balasubramanian“Expert system design usingwavelet and color vocabulary trees for image retrieval, Expert Systems with ApplicationsVolume 39, Issue 5, April 2012, Pages 5104–5114.
Index Terms

Computer Science
Information Sciences

Keywords

Color Histogram Content Based Image Retrieval Fuzzy color histogram and Segmentation

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