Open Access Journal

ISSN : 2456-1304 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Science Engineering and Management (IJSEM)

Monthly Journal for Science Engineering and Management

ISSN : 2456-1304 (Online)

A review on bridging (Reducing) the SG (Semantic Gap) in CBIR and Annotation

Author : Tabassum Nahid Sultana 1 Md Abdul Haleem Bahadur 2 Dr. Ruksar Fatima 3 Mohammed Shakeeb Ahmed 4

Date of Publication :20th May 2016

Abstract: this paper has tried to identify the problems in content based image retrieval technique. The key obstacle of CBIR approaches is the SG i.e., the difference between the low level features to high level concept of human perception. To extend the image retrieval process beyond low level visual descriptors to high level image semantics. Therefore researches proposed different SBIR techniques to bridge the SG using various machine learning algorithms for the extraction of semantic images to increase the accuracy of the image retrieval process

Reference :

    1. R. Priyatharshini , S. Chitrakala,”Association based Image retrieval: A survey,”Springer-Verlag Berlin Heidelberge, pp. 17-26, 2013
    2.  Vaishali D. Dhale , A. R. Mahajan, Uma Thakur, “A Survey of Feature Extraction Methods for Image Retrieval,” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 10, October 2012 ISSN: 2277 128X.
    3. Alaa M. Riad, Hamdy.K. Elminir, SamehAbd-Elghany, “A Literature Review of Image Retrieval based on Semantic Concept,” International Journal of Computer Applications ( 0975 – 8887) Volume 40– No.11, February 2012
    4. Chuan yu chang, hung jen wang, chi fang li. “Semantic analysis of real world images using support vector machine”.Expert systems with application : An interntation journal 2009.ACM
    5. Wei Jiang, Kap luk chan, Mingjing Li. „Mapping low level features to high level semantic concept in region based image retrieval”. IEEE CVPR05. 2005 [6] Yang, L. and Jin, R. (2006). Distance metric learning: A comprehensive survey.
    6. Goldberger, J., Roweis, S., Hinton, G., and Salakhutdinov, R. Neighbourhood components analysis. In NIPS, 2005.
    7. Torresani, L. and Lee, K. Large margin component analysis. In NIPS, 2007. [9] Chad Carson and Virginia E. Ogle. “Storage and retrieval of feature data for a very large online image collection”. IEEE Computer Society Bulletin of the Technical Committee on Data Engineering, 19(4):19–27, December 1996.
    8. Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3 (Mar. 2003).

Recent Article