CFP last date
20 December 2024
Reseach Article

Content based Image Retrieval System with Hybrid Feature Set and Recently Retrieved Image Library

by Seema Haribhau Jadhav, Shah Aqueel Ahmed
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 59 - Number 5
Year of Publication: 2012
Authors: Seema Haribhau Jadhav, Shah Aqueel Ahmed
10.5120/9548-4001

Seema Haribhau Jadhav, Shah Aqueel Ahmed . Content based Image Retrieval System with Hybrid Feature Set and Recently Retrieved Image Library. International Journal of Computer Applications. 59, 5 ( December 2012), 46-55. DOI=10.5120/9548-4001

@article{ 10.5120/9548-4001,
author = { Seema Haribhau Jadhav, Shah Aqueel Ahmed },
title = { Content based Image Retrieval System with Hybrid Feature Set and Recently Retrieved Image Library },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 59 },
number = { 5 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 46-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume59/number5/9548-4001/ },
doi = { 10.5120/9548-4001 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:23.984970+05:30
%A Seema Haribhau Jadhav
%A Shah Aqueel Ahmed
%T Content based Image Retrieval System with Hybrid Feature Set and Recently Retrieved Image Library
%J International Journal of Computer Applications
%@ 0975-8887
%V 59
%N 5
%P 46-55
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content based image retrieval system is a fast growing research area, where the visual content of a query image is used to search images from large scale image databases. In this proposed an effective system, both the semantically and visually relevant features are used to retrieve the related images. The challenge for the CBIR system is how to efficiently capture the features of the query image for retrieval. In traditional content based retrieval system, the visual content features of the whole query image are used for the retrieval purpose. But in the proposed system, the object wise features of query image are utilized for the effective retrieval. Moreover, an active Recently Retrieved Image Library (RRI Library) is used, which increases the accuracy in each retrieval. An RRI library uses an index system, which maintains the recently retrieved images, and during the retrieval process, the proposed system searches the pertinent images from both the database as well as the RRI library and hence the retrieval precision is gradually increased in each retrieval. The proposed CBIR method is evaluated by querying diverse images and the retrieval efficacy is analyzed by calculating the precision-recall values for the retrieval results.

References
  1. Jianhua Wu, Zhaorong Wei and Youli Chang, "Color and Texture Feature for Content Based Image Retrieval", International Journal of Digital Content Technology and its Applications, Vol. 4, No. 3, pp: 43-49, June 2010.
  2. Ch. Srinivasa Rao , S. Srinivas Kumar and B. Chandra Mohan," Content Based Image Retrieval using Exact Legendre Moments and Support Vector Machine", The International Journal of Multimedia and its Applications (IJMA), Vol. 2, No. 2,pp: 69-79, May 2010.
  3. Hiremath and Jagadeesh Pujari, "Content Based Image Retrieval based on Color, Texture and Shape features using Image and its complement", International Journal of Computer Science and Security, Vol. 1, Issue. 4, pp: 25-35, 2007.
  4. Suresh Pabboju and A. Venu Gopal Reddy, "A Novel Approach for Content-Based Image Indexing and Retrieval System using Global and Region Features", International Journal of Computer Science and Network Security, Vol. 9 No. 2, pp: 119-130, Feb 2009.
  5. Ch. Srinivasa rao , S. Srinivas kumar and B. N. Chatterji, "Content Based Image Retrieval using Contourlet Transform", ICGST-GVIP Journal, Volume 7, Issue 3, pp:9-15, November 2007.
  6. Awais Adnan, Muhammad Nawaz, Sajid Anwar, Tamleek Ali and Muhammad Ali, "Object Identification with Color, Texture, and Object-Correlation in CBIR System", World Academy of Science, Engineering and Technology, Vol. 64, pp. 117-122, 2010.
  7. Murthy, Vamsidhar, Swarup Kumar and Sankara Rao, "Content Based Image Retrieval using Hierarchical and K-Means Clustering Techniques", International Journal of Engineering Science and Technology, Vol. 2, No. 3, pp. 209-212, 2010.
  8. G. Sasikala, R. Kowsalya, M. Punithavalli, "A Comparative Study of Dimension Reduction Techniques for Content-Based Image Retrieval", The International journal of Multimedia & Its Applications (IJMA) Vol. 2, No. 3, pp: 40-47, August 2010.
  9. Rajshree S. Dubey, Rajnish Choubey and Joy Bhattacharjee, "Multi Feature Content Based Image Retrieval", International Journal on Computer Science and Engineering, Vol. 2, No. 6, pp: 2145-2149, 2010.
  10. Christoper C. Yang, "Content Based Image Retrieval: a Comparison between Query by Example and Image Brousing Map Approaches", Journal of Information Science, Vol. 30, No. 3, pp: 254-267, 2004.
  11. Hui Hui Wang, Dzulkifli Mohamad, N. A Ismail, "Image Retrieval: Techniques, Challenge, and Trend", World Academy of Science, Engineering and Technology Vol. 60, pp: 716-718, 2009.
  12. Nandagopalan, Adiga and Deepak, "A Universal Model for Content-Based Image Retrieval", World Academy of Science, Engineering and Technology, Vol. 46, 2008.
  13. Srinivasa Rao and Srinivas Kumar, "Content Based Image Retrieval using Contourlet Sub band Decomposition," In Proceedings of IEEE International Conference, SPIT Colloquium, Mumbai, pp. 140-145, February 2008. Thomas M. Deserno, Sameer Antani and Rodney Long, "Ontology of Gaps in Content-Based Image Retrieval", Journal of Digital Imaging, Vol. 22, No. 2, 2009
  14. Hiremath and Jagadeesh Pujari, "Content Based Image Retrieval using Color Boosted Salient Points and Shape features of an image", International Journal of Image Processing, Vol. 2, No. 1, pp. 10-17, 2008.
  15. Mark Ewald, "Content-Based Image Indexing and Retrieval in an Image Database for Technical Domains", Transactions on Machine Learning and Data Mining, Vol. 2, No 1, pp: 3-22, 2009.
  16. Ganapathi Reddy, Babu and Somasekhar, "Image Retrieval by Semantic Indexing", Journal of Theoretical and Applied Information Technology, Vol. 5, No. 6, pp. 745-750, 2005.
  17. Arun Kulkarni, Harikrisha Gunturu and Srikanth Datla, "Association-Based Image Retrieval", WSEAS Transactions on Signal Processing, Vol. 4, No. 4, pp. 183-189, 2008.
  18. Preeti Aggarwal, Sardana and Gagandeep Jindal, "Content Based Medical Image Retrieval: Theory, Gaps and Future Directions", ICGST-GVIP Journal, Vol. 9, No. 2, pp. 27-37, 2009.
  19. Wichian,Premchaiswadi & Anucha Tungkatsathan, "On-line Content-Based Image Retrieval System using Joint Querying and Relevance Feedback Scheme", WSEAS Transactions on Computers, Vol. 9, No. 5, pp. 465-474, 2010.
  20. Hui Hui Wang, Dzulkifli Mohamad and Ismail, "Semantic Gap in CBIR: Automatic Objects Spatial Relationships Semantic Extraction and Representation", International Journal of Image Processing (IJIP), Vol. 4, No. 3, 192-204, 2010.
  21. Nbhan D. Salih and David Chek Ling Ngo, "A Novel Method for Shape Representation", In proceedings of ICGST International Conference on Graphics, Vision and Image Processing (GVIP-05 ), Egypt, December 2005.
  22. Shi Z, Setlur S, Govindaraju V," Digital Enhancement of Palm Leaf Manuscript Images using Normalization Techniques", 5th International Conference On Knowledge Based ComputerSystems, 2004 December 19-22, 2004 Hyderabad, India; 2004.
  23. Surinta O, Nitsuwat S. Handwritten , "Thai Charactr Recognition Using Fourier Descriptors and Robust C-Protytype" , Information Technology Journal, June 2006.
  24. Surinta, O. and Chamchong, R. , "Proceedings in IFIP International Federation for Information Processing", Vol 288, pp. 182–189. 2008.
  25. Ravichandran and Ananthi, "Color Skin Segmentation Using K-Means Cluster", International Journal of Computational and Applied Mathematics, Vol. 4, No. 2, pp. 153-157, 2009.
  26. Vincent and Folorunso, "A descriptive algorithm for sobel image edge detection", in proceedings of Informing Science & IT Education Conference (InSITE), 2009.
  27. Bjornar Larsen and Chinatsu Aone, "Fast and Effective Text Mining Using Linear-time document Clustering", In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, San Diego, California, United States , pp. 16 – 22, 1999.
  28. Michael Steinbach, George Karypis and Vipin Kumar, "A Comparison of Document Clustering Techniques", in proceedings of the KDD-2000 Workshop on Text Mining, Boston, MA, pp. 109-111, 2000.
  29. Thomas M. Deserno, Sameer Antani and Rodney Long, "Ontology of Gaps in Content-Based Image Retrieval", Journal of Digital Imaging, Vol. 22, No. 2, 2009
Index Terms

Computer Science
Information Sciences

Keywords

Content Based Image Retrieval Mean Filter Low level feature High level feature Image Segmentation k-mean algorithm