CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Advance Technique for Feature Extraction and Image Compression

by Arvind Kourav, Prashant Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 68 - Number 21
Year of Publication: 2013
Authors: Arvind Kourav, Prashant Singh
10.5120/11703-7289

Arvind Kourav, Prashant Singh . Advance Technique for Feature Extraction and Image Compression. International Journal of Computer Applications. 68, 21 ( April 2013), 22-27. DOI=10.5120/11703-7289

@article{ 10.5120/11703-7289,
author = { Arvind Kourav, Prashant Singh },
title = { Advance Technique for Feature Extraction and Image Compression },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 68 },
number = { 21 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume68/number21/11703-7289/ },
doi = { 10.5120/11703-7289 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:28:31.182590+05:30
%A Arvind Kourav
%A Prashant Singh
%T Advance Technique for Feature Extraction and Image Compression
%J International Journal of Computer Applications
%@ 0975-8887
%V 68
%N 21
%P 22-27
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For image processing, it is very necessary that the selection of transform. In this paper, a comparative analysis of curve let transform with other transform for image processing . In this we proposed the applications of curve let transform in the field of image Compression ,phase recognition and feature extraction. For higher compression with quality reconstruction . The Wavelets gave a different aspect to the compression. Curvelet Transform gives better results in terms of PSNR. Face recognition is very important for many applications such as: video surveillance, criminal investigations and forensic applications, secure electronic banking, mobile phones, credit cards, secure access to buildings . The curve let transform is a multi scale directional transform, which allows an almost optimal non adaptive sparse representation of objects with edges. Curve let have also proven useful in diverse fields beyond the traditional image processing application, Curvelet transform improve recognition accuracy with featature extraction extraction algorithms PCA, LDA,ICA and NMF.

References
  1. E. Gomathi and K. Baskaran "Face Recognition Fusion Algorithm Based on Wavelet" European Journal of Scientific Research ISSN 1450-216X Vol. 74 No. 3, pp. 450-455,2012
  2. E. Candès and L. Demanet, "The curvelet representation of wave propagators is optimally sparse," Commun. Pure Appl. Math. pp. 1472–1528, vol. 58, no. 11,2005
  3. Nilima D. Maske, Wani V. Patil "Comparison of Image Compression using Wavelet for Curvelet Transform & Transmission over Wireless Channel" International Journal of Scientific and Research Publications, , Issue 5, ISSN 2250-3153 Volume 2, May 2012.
  4. C. Villegas-Quezada and J. Climent, "Holistic face recognition using multivariate approximation, genetic algorithms and adaboost classifier: preliminary results," World Academy of Science, Engineeringand Technology, vol. 44, pp. 802–806, 2008.
  5. L. L. Shen and L. Bai, "Gabor feature based face recognition usingkernal methods," in Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition (FGR '04), vol. 6, pp. 386–389, May 2004
  6. M. Zhou and H. Wei, "Face verification using gabor wavelets and AdaBoost," in Proceedings of the 18th International Conference on Pattern Recognition (ICPR '06), vol. 1, pp. 404–407, August 2006.
  7. S. Chen, X. Tan, Z. H. Zhou, and F. Zhang, "Face recognition from a single image per person: a survey," Pattern Recognition, vol. 39, no. 9, pp. 1725–1745, 2006.
  8. Y. Gao and M. K. H. Leung, "Face recognition using line edge map," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 764–779, 2002.
  9. C. BenAbdelkader and P. Griffin, "A local region-based approach togender classification from face images," in Proceedingsof the IEEE Computer Society Conference on ComputerVision and Pattern Recognition, vol. 3, pp. 52–57, 2005.
  10. M. Turk, A. Pentland, Face Recognition using Eigenfaces, Proc. Computer Vision and Pattern Recognition, pp 586-591,1991.
  11. T. Ahonen, A. Hadid, and M. Pietikainen, "Face description withlocal binary patterns: application to face recognition," The IEEE Transactions on Pattern Analysis andMachine Intelligence,vol. 28, pp. 2037–2041, 2006.
  12. G. C. Feng, P. C. Yuen, D. Q. Dai, Human Face Recognition using PCA on Wavelet Subband, Journal of Electronic Imaging, Vol. 9, Issue 2, pp 226-233, 2000
  13. R. Gottumukkal and V. K. Asari, "An improved face recognition technique based on modular PCA approach," Pattern Recognition Letters, vol. 25, no. 4, pp. 429–436, 2004.
  14. C. C. Liu and D. Q. Dai, "Face recognition using dualtree complex wavelet features," IEEE Transactions on Image Processing, vol. 18, no. 11, pp. 2593–2599, 2009.
  15. Truong T. Nguyen and Hervé Chauris "Uniform Discrete Curvelet Transform" IEEE TRANSACTIONS ON SIGNAL PROCESSING,3618-3634, VOL. 58, NO. 7, JULY 2010.
  16. P S Arun Kumar, "Implementation of Image Compression Algorithm using Verilog with Area, Power and Timing Constraints", 2009.
  17. Balasubramanian, R. ; Bouman, C. A. ; Allebach, J. P. ; "Sequential scalar quantization of vectors:an analysis", IEEE Transactions on Image Processing, Volume: 4, Issue: 9, Page(s): 1282 -1295, 1995.
  18. Ching-Min Cheng; Soo-Chang Pei; "Dependent scalar quantization of color images", IEEE Transactions on Circuits and Systems for Video Technology, Volume: 5 , Issue: 2, Page(s): 124 - 139, 1995.
  19. C;Marmotton F, Ziad,A. O;Oliver,"Wavelet image coding with adaptive dead-zone selection:application to JPEG2000",Proceedings of International Conferenc on Image processing,Volome:3,2002.
Index Terms

Computer Science
Information Sciences

Keywords

Image processing Image Compression Feature Extraction Curvelet transform Wavelet Transform

Powered by PhDFocusTM