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

Content-based Image Retrieval (CBIR) using Hybrid Technique

by Zainab Ibrahim Abood, Israa Jameel Muhsin, Nabeel Jameel Tawfiq
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 83 - Number 12
Year of Publication: 2013
Authors: Zainab Ibrahim Abood, Israa Jameel Muhsin, Nabeel Jameel Tawfiq
10.5120/14500-2307

Zainab Ibrahim Abood, Israa Jameel Muhsin, Nabeel Jameel Tawfiq . Content-based Image Retrieval (CBIR) using Hybrid Technique. International Journal of Computer Applications. 83, 12 ( December 2013), 17-24. DOI=10.5120/14500-2307

@article{ 10.5120/14500-2307,
author = { Zainab Ibrahim Abood, Israa Jameel Muhsin, Nabeel Jameel Tawfiq },
title = { Content-based Image Retrieval (CBIR) using Hybrid Technique },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 83 },
number = { 12 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 17-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume83/number12/14500-2307/ },
doi = { 10.5120/14500-2307 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:59:10.980461+05:30
%A Zainab Ibrahim Abood
%A Israa Jameel Muhsin
%A Nabeel Jameel Tawfiq
%T Content-based Image Retrieval (CBIR) using Hybrid Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 83
%N 12
%P 17-24
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image retrieval is used in searching for images from images database. In this paper, content – based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level co-occurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is concluded that, for the database images used in this work, the CBIR using hybrid technique is better for image retrieval because it has a higher match performance (100%) for each type of similarity measure so; it is the best one for image retrieval.

References
  1. S. Selvarajah and S. R. Kodithuwakku "Combined Feature Descriptor for Content Based Image Retrieval", 2011 6th International Conferencon Industrial and Information Systems, ICIIS 2011, Aug. 16-19, 2011, Sri Lanka, IEEE.
  2. M. Arevalillo-Herr´aez, Francesc J. Ferri, Salvador Moreno-Picot, "An interactive evolutionary approach for content based image retrieval", Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 2009, www. ivsl. org .
  3. A. Eleyan, Hasan Demirel, "Co-occurrence Matrix and its Statistical Features as a New Approach for Face Recognition", Turk J Elec. Eng. & Comp. Sci. , Vol. 19, No. 1, 2011, www. ivsl. org.
  4. Qingwei Gao, Yanfei Zhao, Yixiang Lu, "Despeckling SAR images using stationary wavelet transform combining with directional filter banks", 2008, qingweigao@ahu. edu. cn
  5. S. Ramaswamy and Kenneth Rose, "Fast Adaptive Mahalanobis Distance - Based Search and Retrieval in Image Databases", 2008, www. ivsl. org .
  6. C. Chin Chen ?and H. Ting Chu, "Similarity Measurement between Images", 2005, cchen@cs. nthu. edu. tw
  7. A. A. Goshtasby, Image Registration, "Similarity and Dissimilarity Measures", 2012, ch2.
  8. T. Acharya , Ajoy K. Ray, "Image Processing Principles and Applications", Book, 2005, Published by John Wiley & Sons, Inc. , Hoboken, New Jersey.
  9. T. Deselaers, "Features for Image Retrieval", 2003.
  10. M. Egmont - Petersen, D. de Ridder, H. Handels,"Image Processing with Neural Networks—a review", 2002.
  11. R. Dubey, R. Choubey, S. Dubey, "Efficient Image Mining using Multi Feature Content Based Image Retrieval System", IntJr of Advanced Computer Engineering and Architecture, Vol. 1, No. 1, June 2011, rajshreesanjeevdubey@gmail. com, choubey. rajnish@gmail. com , sanjeevdubey1977@gmail. com
  12. Math Works, Inc. , "Image Processing Toolbox", Matlab 7. 8. 0 (R2009a)", 2009.
  13. B. Wang, Hang-jun Wang, Heng-nian Qi, "Wood Recognition Based on Grey-Level Co-Occurrence Matrix", International Conference on Computer Application and System Modeling (ICCASM 2010), IEEE, www. ivsl. org.
  14. H. B. Kekre, Sudeep D. Thepade, Tanuja K. Sarode and Vashali Suryawanshi, "Image Retrieval using Texture Features extracted from GLCM, LBG and KPE", International Journal of Computer Theory and Engineering, Vol. 2, No. 5, October, 2010,www. ivsl. org
  15. Kanagaraj Kannan, Subramonian Arumuga Perumal, Kandasamy Arulmozhi, "Optimal Decomposition Level of Discrete, Stationary and Dual Tree Complex Wavelet Transform for Pixel based Fusion of Multi-focused Images", Serbian Journal of Electrical Engineering, Vol. 7, No. 1, May 2010, 81-93, Email- kannan_kcet@yahoo. co. in , visvenk@yahoo. co. in , principal@kcetvnr. org.
  16. Michael B. Martin and Amy E. Bell, "New Image Compression Techniques Using Multiwavelets and Multiwavelet Packets", IEEE Transactions on Image Processing, Vol. 10, No. 4, April 2001.
Index Terms

Computer Science
Information Sciences

Keywords

CBIR feature extraction properties color histogram GLCM hybrid similarity measure.

Powered by PhDFocusTM