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Design of an Architecture for Optimizing Image Retrieval by using Genetic Algorithm
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 2 |
| Year of Publication: 2015 |
| Authors: S.selvam, S.thabasukannan |
10.5120/cae-1501
|
S.selvam, S.thabasukannan . Design of an Architecture for Optimizing Image Retrieval by using Genetic Algorithm. Communications on Applied Electronics. 1, 2 ( January 2015), 1-5. DOI=10.5120/cae-1501
Abstract
Image retrieval plays a vital role in image processing. The main aim of this paper is to build more generalized CBIR system which is increased the searching ability and to improve the retrieval accuracy. The proposed method is experimental and analyzed with large database. The result show that the architecture of new CBIR system shown good performance in speed and reducing the computational time.
References
- V. Gudivada and V. Raghavan, "Content-based Image Retrieval Systems," IEEE Computer, vol. 28, no 9, pp18-22, Sep. 1995.
- F. Long,H. Zhang,H. Dagan,andD. Feng, "Fundamentals of Content Based Image Retrieval," Multimedia Signal Processing Book, Chapter1, Springer-Verlag, Berlin Heidelberg New York, 2003.
- S. Selvam and Dr. S. Thabasu Kannan, "Design of an Effective Method for Image Retrieval", published IJIRAE, International Journal of Innovative Research in Advanced Engineering, Volume-1, March 2014, pp. 51-56.
- S. Selvam and Dr. S. Thabasu Kannan, "An Empirical Review on Image Retrieval System by using Relevance Feedback" proceeding of International Symposium on "Research innovation for quality improvement in Higher Education" conducted by Bharathiar University, Coimbatore, October 2014 and published in "Research and Trends in Data mining and Image Processing Technologies and Applications", Bloomsbury publishing India, London, New Delhi, New York, Sydney pp-1-11, October 2014, ISBN: 978-93-84052-11
- R. Chang,J. Ho,S. Lin,C. FannandY. Wang,"ANovelContentBasedImageRetrievalSystemusingK-means with Feature Extraction, "International Conference on Systems and Informatics,2012.
- I. El-Naqa, Y. Yang, N. Galatsanos, R. Nishikawa and M. Wernick, "A Similarity Learning Approach to Content-Based Image Retrieval: Application to Digital Mammography," IEEE Transactions on Medical Imaging, 2009.
- B. WANG,X. ZHANG,andN. LI, "Relevance Feedback Technique For Content-Based Image Retrieval Using Neural Network Learning, "Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 2006.
- R. Datta,J. Li,andJ. Wang,"Content-BasedImageRetrieval-ApproachesandTrends of theNew Age,"ACMComputing Surveys, vol. 40, no. 2, pp. 1-60,April 2008.
- J. HanandM. Kambr,"DataMiningConceptsandTechnique,"2ndEd. ,MorganKaufmannPublisher, 2006.
- P. JeyanthiandV. JawaharSenthil Kumar, "Image Classification by K-means Clustering. "Advances in Computational Sciences and Technology, 2010.
- M. J. Swain and D. H. Ballard, Indexing via Color Hist ograms, ICCV'90, pp. 390- 393, 1990.
- Tat-Seng Chua, Wai-Chee Low, and Chun-Xin Chu, Relevance feedback techniques for color-based image retrieval, In Proceedings of Multi-Media Modeling'98, IEEE Computer Society, pp 24-31, 2011.
- M. J. Swain and D. H. Ballard, Color indexing, International Journal on Computer Vision, vol. 7, no. 1, pp. 11--32, 2011.
- V. E. Ogle and M. Stonebraker, Chabot: retrieval from a relational database of images, IEEE Computer, vol. 28, no. 9, pp. 40-8, Sept. 1995.
- Michael Ortega, Yong Rui, Kaushik Chakrabarti, Sharad Mehrotra and Thomas S. Huang, Supporting Similarity Queries. In Proceeding of the ACM International Multimedia Conference, pp. 403-413, 2013.
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