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A Hybrid Image Mining Technique using LIM-based Data Mining Algorithm

by C. Lakshmi Devasena, M. Hemalatha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 25 - Number 2
Year of Publication: 2011
Authors: C. Lakshmi Devasena, M. Hemalatha
10.5120/3007-4056

C. Lakshmi Devasena, M. Hemalatha . A Hybrid Image Mining Technique using LIM-based Data Mining Algorithm. International Journal of Computer Applications. 25, 2 ( July 2011), 1-5. DOI=10.5120/3007-4056

@article{ 10.5120/3007-4056,
author = { C. Lakshmi Devasena, M. Hemalatha },
title = { A Hybrid Image Mining Technique using LIM-based Data Mining Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 25 },
number = { 2 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume25/number2/3007-4056/ },
doi = { 10.5120/3007-4056 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:41.581188+05:30
%A C. Lakshmi Devasena
%A M. Hemalatha
%T A Hybrid Image Mining Technique using LIM-based Data Mining Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 25
%N 2
%P 1-5
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The field of image retrieval and mining has become a vibrant research area due to speedy enhancement in the volume of digital image databases. Nowadays, a large portion of information is in visual form; it is essential and certainly pleasing to search for images by content. Image mining has a variety of applications in various sectors like medical diagnosis, biology, remote sensing, space research, etc. This research is to determine the exact images while mining an image (multimedia) database and proposes a novel approach for mining images using LIM based image matching technique with neural networks. This process is independent of too many parameter setting to generate a robust solution. It is designed and implemented on MATLAB and is tested with the images of various databases. Appropriate measures were devised to evaluate the performance of the system. The performances of the LIM based image matching technique results were noteworthy and comparable. While comparing with the number of false retrievals with the correct retrievals, the anticipated system performance level will be suited for several simple day to day multimedia database applications and image mining systems. The image mining system derived from the LIM based image matching technique provided promising results.

References
  1. Tiawei Han and Micheline Kamber. 2001 Data Mining Concepts & techniques.
  2. Fafael C.Gonzalez and Richard E. Woods. 1993 Digital Image Processing 2nd edition. Addision Wesley.
  3. C. Lakshmi Devasena, T.Sumathi, Dr. M. Hemalatha 2010 Image/Video Retrieval Technique: Grand Challenges and Trends. Proceedings of National Conference on Applications of Data Mining in National Security, NCOADMINS 2010, ISBN No: 987-93-80697-51-2, 96-105.
  4. C. Lakshmi Devasena, T.Sumathi, Dr. M. Hemalatha 2011 An Experiential Survey on Image Mining Tools, Techniques and Applications. International Journal on Computer Science and Engineering (IJCSE), ISSN : 0975-3397, Vol. 3 No. 3 Mar 2011, 1155 – 1167.
  5. Wynne Hsu, MongLiLee, Ji Zhang, 2002 Image Mining : trends and developments. Journal of Intelligent Information systems, 2002, 7-23.
  6. Ji Zhang, Wynne Hsu, Mong Li Lee. 2001 Image Mining: Issues, Frameworks And Techniques.
  7. Hu Min Yang Shuangyuan 2010 Overview of image mining research. 978-1-4244-6002-1 IEEE Explore, 24-27 Aug. 2010, 1868 – 1870.
  8. Prof. Sharvari Tamane 2008 Content Based Image Retrival using High Level Semantic Features. Proceedings of the 2nd National Conference –INDIA Com 2008.
  9. P. Geetha and Vasumathi Narayanan. 2008 A Survey of Content-Based Video Retrieval. Journal of Computer Science 4 (6), ISSN 1549-3636, 474-486.
  10. Stéphane Marchand-Maillet. 2000 Content-based Video Retrieval: An overview.
  11. McMurray, T. Pearce, J.A. 2002 Theoretical and experimental comparison of the Lorenz information measure, entropy, and the mean absolute error. IEEE Explore ISBN: 0-8186-6250-6, 2002 24-29.
  12. Ki Tai Jeong, Mark Rorvig, Jeho Jeon and Neena Weng 2001 Image Retrieval by Content Measure Metadata Coding.
  13. Syed Ali Khayam. 2003. The Discrete Cosine Transform (DCT): Theory and Application. Department of Electrical & Computer Engineering, Michigan State University.
  14. Andrew B. Watson. 1994 Image Compression Using the Discrete Cosine Transform. Mathematica Journal, 4(1), 81-88.
  15. Wen-Hsiung Chen, Smith. C, Fralick S. 2003 A Fast Computational Algorithm for the Discrete Cosine Transform. 10.1109/TCOM.1977.1093941 IEEE Explore, ISSN: 0090-6778, 25-9 Jan 2003, 1004-1009.
Index Terms

Computer Science
Information Sciences

Keywords

Discrete Cosine Transform Image Mining Image Signature Lorenz Information Measure

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