Google Scholar

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

Published
Jun 10, 2015
Download
PDF
Statistic
Read Counter : 30 Download : 40

Downloads

Download data is not yet available.

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

Abstract

We present an approach for image retrieval using Local Directional Pattern (LDP) and efficient on-line learning. This paper presents an image retrieval using novel local feature descriptor, the Local Directional Pattern (LDP), for describing local image feature. A LDP feature is obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each bit of code sequence is determined by considering a local neighborhood hence becomes robust in noisy situation. A rotation invariant LDP code is also introduced which uses the direction of the most prominent edge response. Finally an image descriptor is formed to describe the image (or image region) by accumulating the occurrence of LDP feature over the whole input image (or image region). Experimental results on the UW database show that LDP impressively outperforms the other commonly used dense descriptors (e.g., Gabor-wavelet and LBP).

Keywords: Grey-Level Co-occurrence matrix(GLCM),Local Binary Pattern (LBP),k-NN,Rotation Invariant LDP.

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

How to Cite
Amit Kumar, M. R. P. (2015). Content Based Image Retrieval Using Local Directional Pattern (LDP) Image Descriptor. International Journal of Emerging Trends in Science and Technology, 2(06). Retrieved from http://igmpublication.org/ijetst.in/index.php/ijetst/article/view/730

References

1. H. Zhou, R. Wanga, and C. Wanga, “A novel extended local-binary-pattern operator for texture analysis”, Information Sciences, vol. 178, no. 22, pp. 4314 – 4325, 2008.
2. T. Jabid, M. H. Kabir, and O. S. Chae, “Local Directional Pattern (LDP) for Face Recognition,” IEEE International Conference on Consumer Electronics, January 2010.
3. T. Jabid, M. H. Kabir, and O. S. Chae, “Gender Classification using Local Directional Pattern (LDP),”Accepted in International Conference on Pattern Recognition, August 2010.
4. J. Zhang and T. Tan, “Brief review of invariant texture analysis methods,” Pattern Recognition, vol. 35, pp.735–747, 2002.
5. T. Maenp¨a¨a¨ and M. Pietikainen,¨ “Texture analysis with local binary patterns,” Handbook of Pattern Recognition and Com-puter Vision, pp. 197–216, 2005 .
6. T. Ojala, K. Valkealahti, E. Oja, and M. Pietikainen,¨ “Texture discrimination with multidimensional distributions of signed gray-level differences,” Pattern Recognition, vol. 34, pp. 727– 739, 2001.
7. 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.
8. 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.
9. Raghu Krishnapuram, Swarup Medasani, Sung-Hwan Jung, Young-Sik Choi,” Content-Based Image pp. Retrieval Based on a Fuzzy Approach” IEEE transactions on knowledge and data engineering, vol.16, no.10, 1185-1199,Oct ,2004.
10. Dionysius P. Huijsmans and Nicu Sebe,” How to Complete Performance Graphs in Content-Based Image Retrieval: Add Generality and Normalize Scope”, IEEE transactions on pattern analysis and machine intelligence,vol. 27, no. 2, pp. 245-251,Feb, 2005.