We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Significance of Context Sensitiveness in Content based Image Retrieval System and Bridging the Semantic Gap

by N. Karthikeyan, R. Dhanapal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 8
Year of Publication: 2013
Authors: N. Karthikeyan, R. Dhanapal
10.5120/14594-2833

N. Karthikeyan, R. Dhanapal . Significance of Context Sensitiveness in Content based Image Retrieval System and Bridging the Semantic Gap. International Journal of Computer Applications. 84, 8 ( December 2013), 8-13. DOI=10.5120/14594-2833

@article{ 10.5120/14594-2833,
author = { N. Karthikeyan, R. Dhanapal },
title = { Significance of Context Sensitiveness in Content based Image Retrieval System and Bridging the Semantic Gap },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 8 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number8/14594-2833/ },
doi = { 10.5120/14594-2833 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:00:22.721863+05:30
%A N. Karthikeyan
%A R. Dhanapal
%T Significance of Context Sensitiveness in Content based Image Retrieval System and Bridging the Semantic Gap
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 8
%P 8-13
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The increase in discovery of faster processors and high capacity storage devices has triggered the development of search beyond plain text. 2D searches have started becoming prevalent, that helps users in obtaining images according to their queries. In this paper, we discuss the principal challenges facing CBIR systems and ways in which they could be overcome. Discussion of various solutions provided for improvising the CBIR technique is discussed. The paper concludes with the directions for future research, along with implementation suggestions that will be helpful during CBIR implementations.

References
  1. Jain and A. Vailaya, Aug. 1996, "Image Retrieval Using Color and Shape,"Pattern Recognit. , Vol. 29, No. 8, pp. 1233–1244.
  2. I. J. Cox, et al. , 2000, "The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. " IEEE, Trans. On Image Processing. 9, p. 20-37.
  3. Weber R, Schek J-J, Blott S, August 1998, "Aquantitative analysis and performance study for similaritysearch methods in high-dimensional space. " In: Proc int'l conf very large databases, New York, 24–27, pp 194–205.
  4. Kaplan LM, Murenzi R, Namuduri KR, 1997, "Fast texture dadabase retrieval using extended fractal features. " Proc SPIE Conf On Electric Imaging: Storage & Retrieval for Image and Video Database IV 3312:162–173, San Jose, CA.
  5. Yang-Hoon Kim & Hyuk-Jun Kwon & Jong-Gu Kang & Hangbae Chang, "The study on content based multimedia data retrieval System," Multimed Tools Appl 57:393–405, DOI 10. 1007/s11042-011-0758-5,2012.
  6. J. Tsotsos, John Wiley and Sons, 1992, "The Encyclopedia of Artificial Intelligence," pp. 641–663,Chapter: Image Understanding.
  7. S. Dickinson,1999,"What is Cognitive Science?", Basil Blackwell Publishers, pp. 172–207, Chapter: Object Representation and Recognition.
  8. L. G. Roberts, Machine Perception of Three Dimensional Solids, Ph. D. thesis, 1963, "Massachusetts Institute of Technology".
  9. L. G. Roberts, 1960,"Pattern recognition with an adaptive network," in: Proc. IRE International Convention Record, pp. 66–70.
  10. Xu, De Xu, and Enai Lin , A. Gagalowicz and W. Philips (Eds. ), Hongli, 2007, "An Applicable Hierarchical Clustering Algorithm for Content-Based Image Retrieval,": MIRAGE 2007, LNCS 4418, pp. 82–92. © Springer-Verlag Berlin Heidelberg 2007
  11. Nguyen-Khang Pham, Annie Morin, Patrick Gros, and Quyet-Thang Le, F. Guillet et al. (Eds. ), "Intensive Use of Correspondence Analysis for Large Scale Content-Based Image Retrieval, Advances in Knowledge Discovery and Management," SCI 292, pp. 57–76.
  12. Tatiana Jaworska1, Janusz Kacprzyk1, Nicolas Mar´?n2, and S_lawomir, Zadro?zny, E. H¨ullermeier, R. Kruse, and F. Hoffmann (Eds. ), 2010, "On Dealing with Imprecise Information in a Content Based Image Retrieval System", IPMU 2010, LNAI 6178, pp. 149–158. Springer-Verlag Berlin Heidelberg 2010
  13. Carlos E. Alvez1 and Aldo R. Vecchietti2, R. Setchi et al. (Eds. ), 2010, "Combining Semantic and Content Based Image Retrieval in ORDBMS,": KES 2010, Part II, LNAI 6277, pp. 44–53.
  14. Gennaro, Giuseppe Amato, Paolo Bolettieri, and Pasquale Savino, M. Lalmas et al. (Eds. ), Claudio 2010,"An Approach to Content-Based Image Retrieval Based on the Lucene Search Engine Library", ECDL, LNCS 6273, pp. 55–66, 2010.
  15. Qinmin Vivian Hu1, Zheng Ye1,2, and Xiangji Jimmy Huang1, A. An et al. (Eds. ),2010, "Enhancing Content-Based Image Retrieval Using Machine Learning Techniques," AMT 2010, LNCS 6335, pp. 383–394.
  16. Chowdhury, Sudeb Das, and Malay Kumar Kundu, M. K. Kundu et al,2012, "Interactive Content Based Image Retrieval Using Ripplet Transform and Fuzzy Relevance Feedback, Manish," Eds. : PerMIn 2012, LNCS 7143, pp. 243–251.
  17. J. T. Tippett, D. A. Borkowitz, L. C. Clapp, C. J. Koester, A. J. Vanderburgh (Eds. ,1965, "Optical and Electro-Optical Information Processing," MIT Press.
  18. M. Ejiri,2007,"Machine vision in early days: Japan's pioneering contributions", in: Proc. 8th Asian Conference on Computer Vision (ACCV).
  19. S. Kashioka, M. Ejiri, Y. Sakamoto,1976,"A transistor wire-bonding system utilizing multiple local pattern matching techniques," IEEE Transactions on Systems, Man and Cybernetics 6 (8) ,562–570.
  20. G. Gallus, "Contour analysis in pattern recognition for human chromosome classification," Appl Biomed Calcolo Electronico 2 1968.
  21. G. Gallus, G. Regoliosi, "A decisional model of recognition applied to the chromosome boundaries," Journal of Histochemistry & Cytochemistry 22 1974.
  22. A. Jimenez, R. Ceres, J. Pons,2000, "A survey of computer vision methods for locating fruits on trees," IEEE Transactions of the ASABE 43 (6),) 1911–1920.
  23. E. N. Malamas, E. G. M. Petrakis, M. Zervakis, L. Petit, J-D. Legat,2003,"A survey on industrial vision systems, applications and tools," Image and Vision Computing 21 (2) 171–188.
  24. 50 Years of object recognition: Directions forward q, Alexander Andreopoulos a,?, John K. Tsotsos b,2013,"Computer Vision and Image Understanding", 117 827–891,http://dx. doi. org/10. 1016/j. cviu. 2013. 04. 005 .
  25. Wang Z, Jia K, Liu P,2009,"An effective web content-based image retrieval algorithm by using SIFT feature. " Softw Eng 1:291–295.
  26. Tian Zhang, Raghu Ramakrishnan, and Miron Livny. BIRCH,1996,"An Efficient Data Clustering Method for Very Large Databases. " In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pages 103-114, Montreal, Canada.
  27. Guha S, Rastogi R, Shim K. CURE ,1998," an efficient clustering algorithm for large databases. " Proc. of the ACM SIGMOD Int'l Conf. on Management of Data. Seattle: ACM Press, 73~84.
  28. Ester M, Kriegel H, Sander J, Xu XW. 1996,"A density-based algorithm for discovering clusters in large spatial databases with noise. " Proc. of the 2nd Int'l Conf. on Knowledge Discovery and Data Mining (KDD'96). Portland: AAAI Press, 226~231.
  29. Hinneburg A, Keim D. 1998,"An efficient approach to clustering in large multimedia databases with noise. " Proc. of the 4th Int'l Conf. on Knowledge Discovery and Data Mining (KDD'98). New York: AAAI Press, 58~65.
  30. Ma S, Wang TJ, Tang SW, Yang DQ, Gao J. ,2003, "A fast clustering algorithm based on reference and density. Journal of Software,"14(6):1089~1095. in china.
  31. Liu Kan, Zhou Xiao Zheng , and Zhou DongRu. , Oct1 2003,"Clustering by Ordering Density-Based Subspaces and Visualization Journal of Computer Research and Development,"Vol. 40, No. 10, 1509~1513,in china.
  32. Wang W, Yang J, Muntz RR. STING:,1997,"A statistical information grid approach to spatial data mining. " Proc. of the 23rd Int'l Conf. on Very Large Data Bases. Athens: Morgan Kaufmann,186~195.
  33. Sheikholeslami G, Chatterjee S, Zhang AD. WaveCluster:,1998,"Amulti-resolution clustering approach for very large spatial databases. " Proc. of the 24th Int'l Conf. on Very Large Data Bases. New York: Morgan Kaufmann, 428~439.
  34. Rakesh A, Johanners G, Dimitrios G, Prabhakar R. 1998,"Automatic subspace clustering of high dimensional data for data mining applications. " Proc. of the 1998 ACM SIGMOD Int'l Conf. on Management of Data. Minneapolis: ACM Press. 94~105.
  35. Amato, G. , Savino, P. 2008:"Approximate similarity search in metric spaces using inverted files. " In: Proceedings of the 3rd International Conference on Scalable Information Systems (InfoScale 2008), pp. 1–10. ICST.
  36. Chavez, E. , Figueroa, K. , Navarro, G,2007: "Effective proximity retrieval by ordering permutations. " IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 1647–1658.
  37. Zhen Jis and Arjjuna Balasuriya,2004,"Bayesian Learning for Object Based Image . " Proceedings of the 2004 IEEE, Conference on Cybernetics and Intelligent Systems Singapore, 1-3 December.
  38. Abhishek Singh, Padmini Jaikumar and Suman K Mitra, "A Bayesian Learning Based Approach for Clustering of Satellite ImagesSixth Indian Conference on Computer Vision," Graphics & Image Processing, 978-0-7695-3476-3/08 $25. 00 © 2008 IEEE, DOI 10. 1109/ICVGIP. 2008. 60
  39. Mr. Pankaj K. Bharne, Mr. V. S. Gulhane, Miss. Shweta K. Yewale, "Data Clustering Algorithms B ased On Swarm Intelligence," 978-1-4244-8679-3/11/$26. 00 ©2011 IEEE
  40. Marco Dorigo and Thomas St¨utzle ,"Book Review: Ant Colony Optimization," Genetic Programming and Evolvable Machines,Published by: MIT Press, 2004, ISBN 0-262-04219-3, 328 pages, 6, 459–460, 2005
  41. Muhammad Shaheen, Muhammad Shahbaz, Aziz Guergachi, "Context based positive and negative spatio-temporal association rule mining," Knowledge-Based Systems 37 (2013) 261–273
  42. N. Karthikeyan, Dr. R. Dhanapal,2013,"Image Retrieval of Domain Name System Space Adjustment Technique". International Journal of Computing, Vol. 5, No. 3, pp. 34 – 38.
Index Terms

Computer Science
Information Sciences

Keywords

CBIR Semantic Gap Context Sensitive Retrieval Load Balancing Parallelization Techniques