Google Scholar

##plugins.themes.academic_pro.article.sidebar##

Published
Jul 8, 2015
Download
PDF
Statistic
Read Counter : 30 Download : 25

Downloads

Download data is not yet available.

##plugins.themes.academic_pro.article.main##

Abstract

Content-based image retrieval (CBIR) has been more and more important in the last decade. Visual information systems are radically different from conventional information systems. Many novel issues need to be addressed. A visual information system should be capable of providing access to the content of image. Where symbolic and numerical information are identical in content and form, images require a delicate treatment to approach their content. To search and retrieve items on the basis of their content requires a new visual way of specifying the query, new indices to order the data and new ways to establish similarity between the query and the target. In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation. We also discuss some of the key challenges involved in the benchmark datasets and adaptation of existing image retrieval techniques to build useful systems.

Keywords: Annotation, Content-based image retrieval (CBIR), Feature Extraction, Query learning, Support vector machines (SVM).

##plugins.themes.academic_pro.article.details##

Author Biography

Meenakshi, Ruby Panwar, Amit Kumar, Ajmer Institute of Technology, Ajmer

Asst. Professor, Department of Computer Science
How to Cite
Amit Kumar, M. R. P. (2015). Approaches and Trends in Content Based Image Retrieval System. International Journal of Emerging Trends in Science and Technology, 2(07). Retrieved from https://igmpublication.org/ijetst.in/index.php/ijetst/article/view/786

References

1. Rafael C.Gonzalez,Richard E. Woods,” Digital Image Processing” Pearson Education, Third Edition.
2. A. Laine and J. Fan, “Texture classification by wavelet packet signatures,” IEEE TPAMI, vol. 15, pp. 1186–1191, 1993.
3. Y. Rui, T. S. Huang, S.-F. Chang, Image retrieval:Past, present and future, in: M. Liao (Ed.), Proceedings of the International Symposium on Multimedia Information Processing,Taipei, Taiwan, 1997.
4. Zhang, D.S., Wong, A., Indrawan, M., Lu, G., 2000. Content based image retrieval using Gabor texture features. In: Proc.of 1st IEEE Pacific Rim Conference on Multimedia (PCM’00), pp. 392–395
5. J. P. Eakins, M. E. Graham, content based image retrieval, Tech. Rep. JTAP{039, JISC Technology Application Program, Newcastle upon Tyne (2000).
6. Deselaers T. “Features for Image Retrieval”, Master’s thesis, Human Language Technology and Pattern Recogn-ition Group, RWTH Aachen University, Aachen, Germany,2003,p.no 25-29.
7. Keysers D, Deselaers T, Golan C, Ney H. “Deformation Models for Image Recognition”,IEEE Transactions on Pattern Analysis and Machine Intelligence,2007, 29(8), p no. 1422–1435.
8. P. Hong, Q. Tian, and T. S. Huang, “Incorporate support vector machines to content-based image retrieval with relevant feedback” in Proc.Int. Conf. Image Process-ing, vol. 3, Vancouver, BC, Canada, 2000, pp.750–753.
9. Y. Q. Chen, X. Z. Zhou, and T. S. Huang, “One-class SVM for learning in image retrieval” in Proc. IEEE Int. Conf. Image Processing, vol. 1,Thessaloniki, Greece, 2001, pp. 34–37.
10. J. M. Keller, M. R. Gray, and J. A. Givens, “A fuzzy k-nearest neighbor algorithm” IEEE Transaction, Syst., Man Cybern., Apr. 1985, vol. SMC-15, no. 4, pp. 580–585.
11. L. Davis, S. Johns, and J. Aggarwal, “Texture analysis using generalized co-occurrence matrices” , IEEE TPAMI, vol. 1, pp.251–259, 1979.
12. Meenakshi, Ruby Panwar, Amit Kumar “Content Based Image Retrieval Using Local Directional Pattern (LDP) Image Descriptor” International Journal of Emerging Trends in Science and Technology, June 2015,vol. 2,issue-6,pp.2569-2574.