CFP last date
22 April 2024
Reseach Article

Introducing New Meta Search Model through Content based Image Retrieval Algorithm

by Kanak Giri, Kapil Sharma, Pankaj Dadheech
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 13
Year of Publication: 2012
Authors: Kanak Giri, Kapil Sharma, Pankaj Dadheech
10.5120/8261-1801

Kanak Giri, Kapil Sharma, Pankaj Dadheech . Introducing New Meta Search Model through Content based Image Retrieval Algorithm. International Journal of Computer Applications. 52, 13 ( August 2012), 16-18. DOI=10.5120/8261-1801

@article{ 10.5120/8261-1801,
author = { Kanak Giri, Kapil Sharma, Pankaj Dadheech },
title = { Introducing New Meta Search Model through Content based Image Retrieval Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 13 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 16-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number13/8261-1801/ },
doi = { 10.5120/8261-1801 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:52:08.554409+05:30
%A Kanak Giri
%A Kapil Sharma
%A Pankaj Dadheech
%T Introducing New Meta Search Model through Content based Image Retrieval Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 13
%P 16-18
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents an approach to model content based meta-search engine which search all the content related images present in the dataset. Searching through the keywords in an image database require a lot of Meta data (keywords about the image) to be stored for each image in a separate database. This does not lead to an effective search mechanism. This mechanism basically works for the relevant output for the input query image. The process based on the image extraction by the implemented functions like "edge histogram", "image feature extraction i. e. sharpness, smoothness, color etc.

References
  1. John Eakins, Margaret Graham, "Content-based Image Retrieval", University of Northumbria at Newcastle, Technical Report, 1999.
  2. Pentland A et al (1996) "Photobook: tools for content-based manipulation of image databases" International Journal of Computer Vision 18(3), 233-254.
  3. Using relevance feedback with short-term memory for content-based spine X-ray image retrieval XiaoqianXu, Dah-JyeLee, SameerK. Antani, L. RodneyLong, JamesK. Archibald 2010. pp. 1-4.
  4. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing, Pearson Prentice Hall, 2004.
  5. LeninaBirgale, ManeshKokare, DharmpalDoye, "Colour and Texture Features for Content Based Image Retrieval," International Conference on Computer Graphics, Imaging and Visualisation (CGIV'06), pp. 1-4, 2006.
  6. MouradOussalah "Content Based Image Retrieval: Review of State of Art and Future Directions," Image Processing Theory, Tools &Applications,pp. 1-10, 2008.
  7. Nguyen HuuQuynh, Ngo Quoc Tao, Ngo Truong Giang "An efficient method for content based image retrieval using histogram graph," 10th Intl. Conf. on Control, Automation, Robotics and Vision, pp. 874-878, 2008.
  8. RitendraDatta, Dhiraj Joshi, Jia Li and James Z. Wang, "Image Retrieval: Ideas, Influences, and Trends of the New Age," ACM Computing Surveys, vol. 40, 2007.
  9. Dr. N. Krishnan, M. SheerinBanu, C. CallinsChristiyana, "Content Based Image Retrieval using Dominant Color Identification Based on Foreground Objects", International Conference on Computational Intelligence and Multimedia Applications, pp. 190-194, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Content based image retrieval IMAGINGMSE Color Histogram Edge Histogram Correlation Matching heuristic Image Feature Extraction