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Wavelet and Co-occurrence Matrix based Rotation Invariant Features for Texture Image Retrieval using Fuzzy Logic

by Shailendrakumar M. Mukane, Dattatraya S. Bormane, Sachin R. Gengaje
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 24 - Number 7
Year of Publication: 2011
Authors: Shailendrakumar M. Mukane, Dattatraya S. Bormane, Sachin R. Gengaje
10.5120/2952-3973

Shailendrakumar M. Mukane, Dattatraya S. Bormane, Sachin R. Gengaje . Wavelet and Co-occurrence Matrix based Rotation Invariant Features for Texture Image Retrieval using Fuzzy Logic. International Journal of Computer Applications. 24, 7 ( June 2011), 1-5. DOI=10.5120/2952-3973

@article{ 10.5120/2952-3973,
author = { Shailendrakumar M. Mukane, Dattatraya S. Bormane, Sachin R. Gengaje },
title = { Wavelet and Co-occurrence Matrix based Rotation Invariant Features for Texture Image Retrieval using Fuzzy Logic },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 24 },
number = { 7 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume24/number7/2952-3973/ },
doi = { 10.5120/2952-3973 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:19.750794+05:30
%A Shailendrakumar M. Mukane
%A Dattatraya S. Bormane
%A Sachin R. Gengaje
%T Wavelet and Co-occurrence Matrix based Rotation Invariant Features for Texture Image Retrieval using Fuzzy Logic
%J International Journal of Computer Applications
%@ 0975-8887
%V 24
%N 7
%P 1-5
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, research carried out to test the wavelet and co-occurrence matrix based features for rotation invariant texture image retrieval using fuzzy logic classifier. Energy and Standard Deviation features of DWT coefficients up to fifth level of decomposition and eight features are extracted from co-occurrence matrix of whole image and each sub-band of first level DWT decomposition. The texture image is rotated in six different angle. Each rotated texture image sampled to the 128x128, and 256x256 size. The suitability of features are tested using a fuzzy logic classifier. The performance is measured in terms of Success Rate. Success rate is calculated for each rotated texture samples and each of the feature sets. The minimum number of features required for maximum average success rate is obtained. The research shows that for samples of 256x256 size, excellent success rate is achieved for all rotation angle with Wavelet Statistical Features (WSF) as well as Wavelet Co-occurrence Features (WCF). Also energy features perform better than standard deviation features for every rotation angle considered. Also worst case analysis shows that energy features never fail to classify for any of the texture image and more consistent than other features, during the experiment. 8 co-occurrence feature set performs better than 5 co-occurrence feature set. For both the types of features performance degrades in case of 128x128 sample size.

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Index Terms

Computer Science
Information Sciences

Keywords

Discrete Wavelet Transform Wavelet Statistical features Wavelet Co-occurrence matrix features Rotation Invariance Fuzzy Classifier

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