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Multiwavelet based Texture Features for Content based Image Retrieval

by P.V.N.Reddy, K.Satya Prasad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 17 - Number 1
Year of Publication: 2011
Authors: P.V.N.Reddy, K.Satya Prasad
10.5120/2182-2753

P.V.N.Reddy, K.Satya Prasad . Multiwavelet based Texture Features for Content based Image Retrieval. International Journal of Computer Applications. 17, 1 ( March 2011), 39-44. DOI=10.5120/2182-2753

@article{ 10.5120/2182-2753,
author = { P.V.N.Reddy, K.Satya Prasad },
title = { Multiwavelet based Texture Features for Content based Image Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 17 },
number = { 1 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume17/number1/2182-2753/ },
doi = { 10.5120/2182-2753 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:32.288934+05:30
%A P.V.N.Reddy
%A K.Satya Prasad
%T Multiwavelet based Texture Features for Content based Image Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 17
%N 1
%P 39-44
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content based image Retrieval has become one of the most active research areas in the past few years .CBIR system using multiwavelet based features with high retrieval rate and less computational complexity is proposed in this paper. Multiwavelets offer simultaneous orthogonality, symmetry and short support. This property made it a powerful tool for feature extraction of images in the database. Texture features are obtained by computing the energy, standard deviation and mean on each sub band of the multiwavelet decomposed image. To check the retrieval performance texture database of 999 texture images are taken from brotatz album. We have done the comparison of results using mean, energy and standard deviation features and performed that standard deviation gives better results than mean and energy features .Euclidean distance, Canberra distance and Manhattan distance is used as similarity measure in the proposed CBIR system

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Index Terms

Computer Science
Information Sciences

Keywords

CBIR feature extraction multiwavelet transform standard deviation Euclidean distance & Canberra distance Manhattan distance

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