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Reseach Article

An Efficient Approach for Content based Image Retrieval using SVM, KNN-GA as Multilayer Classifier

by Vinay Kumar Lowanshi, Shweta Shrivastava, Vineet Richhariya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 21
Year of Publication: 2014
Authors: Vinay Kumar Lowanshi, Shweta Shrivastava, Vineet Richhariya
10.5120/19144-0558

Vinay Kumar Lowanshi, Shweta Shrivastava, Vineet Richhariya . An Efficient Approach for Content based Image Retrieval using SVM, KNN-GA as Multilayer Classifier. International Journal of Computer Applications. 107, 21 ( December 2014), 43-48. DOI=10.5120/19144-0558

@article{ 10.5120/19144-0558,
author = { Vinay Kumar Lowanshi, Shweta Shrivastava, Vineet Richhariya },
title = { An Efficient Approach for Content based Image Retrieval using SVM, KNN-GA as Multilayer Classifier },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 21 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 43-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number21/19144-0558/ },
doi = { 10.5120/19144-0558 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:41:43.540575+05:30
%A Vinay Kumar Lowanshi
%A Shweta Shrivastava
%A Vineet Richhariya
%T An Efficient Approach for Content based Image Retrieval using SVM, KNN-GA as Multilayer Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 21
%P 43-48
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The technology is growing day by day in various fields and image retrieval is one of the most of them, it is more interesting and fastest growing research areas. It is an effective and efficient tool for managing large image databases. In most Content-Based Image Retrieval (CBIR) systems, images are represented and differentiated by a set of low-level visual features; hence a direct correlation with high-level semantic information will be absent. Therefore, a gap exists between high-level information. In this paper they proposed novel approach for content based image retrieval was two tier architecture model is used for most accurate retrieval. In the first tier first feature extraction process done using PSO with SVM classifier, after successful classification in first tier the retrieved result has been passed into the second tier classifier. And in the second tier KNN classifier is used but as they knew that GA is one of the optimization technique and it produces the best optimized result in maximum cases so it is applied with the KNN classifier, and it produces more accurate and efficient compared result.

References
  1. Ying Liua, Dengsheng Zhang, Guojun Lu,Wei-Ying Ma, "A survey of content-based image retrieval with high-level semantics" Pattern Recognition 40 (2007) 262– 282.
  2. Jefersson Alex dos Santos, Cristiano Dalmaschio Ferreira, and Ricardo da Silva Torres, "A Genetic Programming Approach for Relevance Feedback in Region-based Image Retrieval Systems",
  3. Hatice Cinar Akakin and Metin N. Gurcan, "Content-Based Microscopic Image Retrieval System for Multi-Image Queries", IEEE transactions on information technology in biomedicine, vol. 16, no. 4, July 2012
  4. S. Manoharan, S. Sathappan, "a novel approach for content based Image retrieval using hybrid filter Techniques", the 8th international conference on Computer science & education (ICCSE 2013) April 26-28, 2013. Colombo, Sri lanka.
  5. Khadidja Belattar, Sihem Mostefai "CBIR using Relevance Feedback:Comparative Analysis and Major Challenges" Computer Science Department MISC Laboratory Mentouri University Constantine , Algeria, 5th International Conference on Computer Science and Information Technology 2013.
  6. Lianze Ma, Lin Lin, Mitsuo G, "A PSO-SVM Approach for Image Retrieval and Clustering", Proceedings of the 41st International Conference on Computers & Industrial Engineering.
  7. Pooja Kamavisdar, Sonam Saluja, Sonu Agrawal. "A survey on image classification approaches and techniques", Department of Computer Science & Applications, SSCST, Bhilai, India, IJARCCE, Vol. 2, Issue. 1, Jan 2013.
  8. John Moustakas, Kostas Marias, Socrates Dimitriadis, Stelios C. Orphanoudakis, "A Two-Level Cbir Platform with Application to Brain MRI Retrieval"
  9. S. Kulkarni, B. Verma1, P. Sharma and H. Selvaraj "Content Based Image Retrieval using a Neuro-Fuzzy Technique" 2005.
  10. Mohd. Danish, Ritika Rawat, Ratika Sharma, "A Survey: Content Based Image Retrieval Based On Color, Texture, Shape & Neuro Fuzzy", Int. Journal of Engineering Research and Applications ISSN: 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, Pp. 839-844
  11. Jaya Jeswani1, Tanuja Sarode2, "A Hybrid DCT and DWT Color Image Watermarking in RGB Color Space", International Journal of Computer Science and Information Technologies, Vol. 5 (3), 2014, 3132 – 3138
  12. T. B¨ack, D. B. Fogel, and Z. Michalewicz, "Evolutionary Computation 1 Basics Algorithm and Operators" Institute of Physics Publishing, 2002.
  13. Saurabh Agrawal, Nishchal K Verma, Prateek Tamrakar, Pradip Sircar, "Content based color image classification using SVM", Department of Electrical Engg. , IIT, Kanpur, India, 2011 8th international conference on information technology.
  14. Fakouri R, Zamani, B. ; Fathy, M. ; Minaei, B. , "Region-Based Image Clustering and Retrieval Using Fuzzy Similarity and Relevance Feedback", International Conference on Computer and Electrical Engineering, 2008, Page(s):383 - 387Print ISBN:978-0-7695-3504-3
  15. Kwang-Kyu Seo, "A GA-Based Feature Subset Selection and Parameter Optimization of Support Vector Machine for Content – Based Image Retrieval", Advanced Data Mining and Applications Lecture Notes in Computer Science Volume 4632, 2007, pp 594-604.
Index Terms

Computer Science
Information Sciences

Keywords

Content based image retrieval (CBIR) feature extraction SVM PSO KNN GA object optimization.