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

Performance Evaluation of Texture based Image Retrieval

by P. S. Malge, Pasnur M. A
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 2
Year of Publication: 2013
Authors: P. S. Malge, Pasnur M. A
10.5120/12467-8840

P. S. Malge, Pasnur M. A . Performance Evaluation of Texture based Image Retrieval. International Journal of Computer Applications. 72, 2 ( June 2013), 26-40. DOI=10.5120/12467-8840

@article{ 10.5120/12467-8840,
author = { P. S. Malge, Pasnur M. A },
title = { Performance Evaluation of Texture based Image Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 2 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 26-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number2/12467-8840/ },
doi = { 10.5120/12467-8840 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:36:51.826799+05:30
%A P. S. Malge
%A Pasnur M. A
%T Performance Evaluation of Texture based Image Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 2
%P 26-40
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a novel content based image retrieval (CBIR) system based on Haar Wavelet Transform. Content Based Image Retrieval (CBIR) has been an active research area. The CBIR is to retrieve the images based on a query image, which is specified by content, from the given collection of images. Current system uses texture as a visual content for feature extraction. The present work uses modified Haar wavelet transformation for feature extraction of an image. Here Haar wavelets constructs the feature vector of size ten, characterizing texture feature of the images, only in three iterations of the wavelet transforms. The K Means Clustering Algorithm is then used to cluster the group of images based on feature vector of images by considering the minimum Euclidean distance. The performance evaluation of the present method is done by Precision and Recall for different databases.

References
  1. S. Saha Ray, "A New Wavelet Operational method using Block Pulse and Haar Functions for Numerical Solution of a Fractional Partial Differential Equation", Journal of Fraction Calculus and Applications, 2011.
  2. Piotr Porwik, Agnieszka Lisowska, "The Haar Wavelet Transform in Digital Image Processing: Its Status and Achievements".
  3. Phang Chang, Phang Piau, "Simple Procedure for the Designation of Haar Wavelet Matrices for Differential Equations", Proceeding of the International Multiconference of Engineers and Computer Scientists 2008 Vol II, March 2008.
  4. Patrick J. Van Fleet, " Discrete Haar Wavelet Transform", PREP, Wavelet Workshop 2006.
  5. Anuj Bhardwaj and Rashid Ali, " Image Compression using Modified Fast Haar Wavelet Transform", World Applied Sciences Journal 7 (5): 647-653, 2009.
  6. P. Raviraj and M. Y. Sanavullah, "The Modified 2D-Haar Wavelet Transformations in Image Compression", Middle-East Journal of Scientific Research 2 (2): 73-78, 2007.
  7. Kamrul Hasan Talukder and Koichi Harada, "Haar Wavelet Based approach for Image Compression and Quality Assessment of Compressed Image", IAENG International Journal of Applied Mathematics.
  8. Rohit Arora, Madan Lal Sharma, Nidhika Birla, Anjali Bala, "An Algorithm for image compression using 2D Wavelet Transform", International Journal of Engineering Science and Technology (IJEST).
  9. N. Gnaneshwara Rao, Dr. V. Vijaya Kumara, V Venkata Krishna, "Texture Based Image Indexing and Retrieval", IJCSNS International Journal of Computer Science and Network Security, Vol. 9 No. 5, May 2009.
  10. Iman Makaremi, "Two Dimensional Wavelet and its Applications".
  11. Emily Brown, Samuel Picton Drake, Anthony Finn, "Wavelet Decomposition for Discrete Probability Maps" ,Department of Defence, Australian Government
  12. Hossein Nezamabadi - pour and Saeid Saryazdi, "Object – Based Image Indexing and Retrieval in DCT Domain using Clustering Techniques", World Academy of Science, Engineering and Technology, 2005.
  13. Dengsheng Zhang, Aylwin Wong, Maria Indrawan, Guojun Lu, "Content - based Image Retrieval Using Gabor Texture Features".
  14. Yi Yang, Dong Xu, Feiping Nie, Shuicheng Yan, Yue Ting Zhuang, "Image Clustering using Local Discriminant Models and Global Integration".
  15. Anil K. Jain, "Data Clustering: 50 Years Beyond K-Means".
  16. Vanita G. Tonge, " Content Based Image Retrieval by K- Means "Clustering Algorithm" , International Journal of Engineering Science and Technology (IJEST).
  17. Cluster Analysis.
  18. Ashish Kumar Raikwar, Satbir Jain, " Content based Image Retrieval using Clustering", International Journal of Computer Applications, Vol. 41 No. 21, March 2012.
  19. Leonard Kaufman, Peter J. Rousseeuw, "Finding Groups In Data: An Introduction to Cluster Analysis", Wiley Interscience, A John Wiley & Sons, Inc. , Publication.
  20. P. W. Huang, S. K. Dai, "Image retrieval by texture similarity", The Journal of Pattern Recognition Society, March 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Content based Image Retrieval Haar Wavelet Transform Feature Extraction Wavelet Transform K Means Clustering Algorithm

Powered by PhDFocusTM