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Reseach Article

Image Retrieval using 2D Dual-Tree Discrete Wavelet Transform

by N S T Sai, R C Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 14 - Number 6
Year of Publication: 2011
Authors: N S T Sai, R C Patil
10.5120/1891-2513

N S T Sai, R C Patil . Image Retrieval using 2D Dual-Tree Discrete Wavelet Transform. International Journal of Computer Applications. 14, 6 ( February 2011), 1-7. DOI=10.5120/1891-2513

@article{ 10.5120/1891-2513,
author = { N S T Sai, R C Patil },
title = { Image Retrieval using 2D Dual-Tree Discrete Wavelet Transform },
journal = { International Journal of Computer Applications },
issue_date = { February 2011 },
volume = { 14 },
number = { 6 },
month = { February },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume14/number6/1891-2513/ },
doi = { 10.5120/1891-2513 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:02:55.054553+05:30
%A N S T Sai
%A R C Patil
%T Image Retrieval using 2D Dual-Tree Discrete Wavelet Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 14
%N 6
%P 1-7
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The large amount of image collections available from a variety of sources has posed increasing technical challenges to computer systems to store/transmit and index/manage the image data to make such collections easily accessible. Here to search and retrieve the expected images from the database we need Content Based Image Retrieval system. This paper proposes a new feature vector based on 2D Dual-tree Discrete Wavelet Transform. One of the advantages of the dual-tree complex wavelet transform is that it can be used to implement 2D wavelet transforms that are more selective with respect to orientation than is the separable 2D DWT. Most of the natural images have short span high frequencies and low frequencies extending for larger span. Hence, the design of our feature vector is such that it provides higher spatial localization and lower frequency resolution at higher frequencies and the reverse for lower frequencies. The energy and mean of the frequency content of the image at various sub bands and different spatial resolution (higher for higher frequency bands) is stored as feature vector. Thus, the given feature vector encodes high frequency information as well.

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Index Terms

Computer Science
Information Sciences

Keywords

CBIR DDWT Wavelet Transform Precision Recall Euclidean Distance