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

A Method for Image Retrieval using Combination of Color and Frequency Layers

by Mohammad Reza Azodinia, andras Hajdu
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 3
Year of Publication: 2015
Authors: Mohammad Reza Azodinia, andras Hajdu
10.5120/20724-3075

Mohammad Reza Azodinia, andras Hajdu . A Method for Image Retrieval using Combination of Color and Frequency Layers. International Journal of Computer Applications. 118, 3 ( May 2015), 10-13. DOI=10.5120/20724-3075

@article{ 10.5120/20724-3075,
author = { Mohammad Reza Azodinia, andras Hajdu },
title = { A Method for Image Retrieval using Combination of Color and Frequency Layers },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 3 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 10-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number3/20724-3075/ },
doi = { 10.5120/20724-3075 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:00:40.874490+05:30
%A Mohammad Reza Azodinia
%A andras Hajdu
%T A Method for Image Retrieval using Combination of Color and Frequency Layers
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 3
%P 10-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a fast and effective noise-resistant method for image retrieval has been proposed. In this method, first, the image is decomposed into different frequency layers using complex wavelet transform so as to make it possible to extract the texture features of the image. Thereafter, in the HSV color space, each layer is quantized into 166 different colors and the color histogram is calculated for each layer. Furthermore, a number of statistical features are extracted from each sub-image using complex wavelet transform, which are used along with other features for image retrieval. In order to verify the effectiveness of the proposed method, it has been evaluated using a dataset containing 3000 images and compared to a competent method in this field. The results prove the superiority of the proposed method.

References
  1. H. Muller, N. Michoux, D. Bandon, and A. Geissbuhler. A Review of Content-Based Image Retrieval Systems in Medical Applications – Clinical Benefits and Future Directions. International Journal of Medical Informatics, 73(1):1–23, Feb2004.
  2. J. S. Hong, H. Y. Chen, and J. Hsiang. A Digital Museum of Taiwanese Butterflies. In Proceedings of the Fifth ACM Conference on Digital Libraries, pages 260–261, San Antonio, Texas, United States, 2000. ACM Press.
  3. L. D. Bergman, V. Castelli, and C. Li. Progressive Content-Based Retrieval from Satellite Image Archives. D-Lib Magazine, 3(10), October1997.
  4. B. Zhu, M. Ramsey, and H. Chen. Creating a Large-Scale Content-Based Air photo Image Digital Library. IEEE Transactions on Image Processing, 9(1):163–167, January2000.
  5. J. C. French, A. C. Chapin, and W. N. Martin. An Application of Multiple Viewpoints to Content-based Image Retrieval. In Proceedings of the 3rd ACM/IEEE-CS joint conference on digital libraries, pages 128–130, Washington, DC, USA, 2003.
  6. J. Z. Wang and Y. Du. Scalable integrated region-based image retrieval using irm and statistical clustering. In Proceedings of the 1st ACM/IEEE-CS joint conference on digital libraries, pages 268–277, 2001.
  7. Y. Wang, F. Makedon, J. Ford, L. Shen, and D. Goldin. Generating fuzzy semantic metadata describing spatial relations from images using the r-histogram. In Proceedings of the 4th ACM/IEEE-CS joint conference on digital libraries, pages 202–211, 2004.
  8. R. S. Torres, C. B. Medeiros, M. A. Goncalves, and E. A. Fox. A Digital Library Framework for Biodiversity Information Systems. International Journal on Digital Libraries, 6(1):3–17, February 2006.
  9. Y. Rui and T. S. Hung, Image retrieval: current technique promising directions and open issues, Journal of Visual Communication and Image Representation, vol. 10, pp. 39-62, 1999.
  10. M. Swain and D. Ballard, "Color indexing," Int. J. Comput. Vis. , vol. 7, pp. 11-32, 1991.
  11. G. Pass and R. Zabih, "Histogram refinement for content – based image retrieval," in Proc. IEEE Workshop on Applications of Computer Vision, 1996, pp. 96-102.
  12. J. Huang et al. , "Spatial color indexing and applications," Int. J. Comput. Vis. , pp. 245-268, 1999.
  13. A. Mojsilovic et al. , "Matching and retrieval based on the vocabulary and grammar of color patterns," IEEE Trans. Image Processing, vol. 9, pp. 38-54, Jan. 2000.
  14. J. R. Smith and S. F. Chang, "VisualSeek: A fully automated content-based image query system", Proc. Int. Conf. on Image Proceeding, 1996.
  15. B. S. Manjunath and W. Y. Ma, "texture features for browsing and retrieval of image data", IEEE Trans. Patt. Anal. Mach. Int. Special Issue on Digital Libraries, Vol. 18, No. 8, pp. 837-842, August 1996.
  16. Abolfazl AleAhmad , Hadi Amiri , Ehsan Darrudi , Masoud Rahgozar , Farhad Oroumchian, Hamshahri: A standard Persian text collection, Journal of Knowledge-Based Systems, Vol. 22 No. 5, p. 382-387, Elsevier, July 2009.
  17. H. Muller, W. Muller, D. M. Squire, S. M. Mailent and T. Pun, Performance evaluation in contentbased image retrieval: overview and proposals, Pattern Recognition Letters, vol. 22, pp. 593-601, 2001.
  18. http://hadoop. apache. org/
  19. D. Jeffrey, S. Ghemawat. MapReduce: Simplified data processing on large clusters. Symposium on Operating System Design and Implementation, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Color feature complex wavelet transform Content-based image retrieval feature extraction histogram image processing and texture feature.

Powered by PhDFocusTM