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Image Retrieval using Fractional Coefficients of Orthogonal Wavelet Transformed Images with Seven Image Transforms

by Dr. H. B. Kekre, Dr. Sudeep D. Thepade, Varun K. Banura, Aanchal Bhatia
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 30 - Number 1
Year of Publication: 2011
Authors: Dr. H. B. Kekre, Dr. Sudeep D. Thepade, Varun K. Banura, Aanchal Bhatia
10.5120/3608-5016

Dr. H. B. Kekre, Dr. Sudeep D. Thepade, Varun K. Banura, Aanchal Bhatia . Image Retrieval using Fractional Coefficients of Orthogonal Wavelet Transformed Images with Seven Image Transforms. International Journal of Computer Applications. 30, 1 ( September 2011), 14-20. DOI=10.5120/3608-5016

@article{ 10.5120/3608-5016,
author = { Dr. H. B. Kekre, Dr. Sudeep D. Thepade, Varun K. Banura, Aanchal Bhatia },
title = { Image Retrieval using Fractional Coefficients of Orthogonal Wavelet Transformed Images with Seven Image Transforms },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 30 },
number = { 1 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 14-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume30/number1/3608-5016/ },
doi = { 10.5120/3608-5016 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:16:55.536026+05:30
%A Dr. H. B. Kekre
%A Dr. Sudeep D. Thepade
%A Varun K. Banura
%A Aanchal Bhatia
%T Image Retrieval using Fractional Coefficients of Orthogonal Wavelet Transformed Images with Seven Image Transforms
%J International Journal of Computer Applications
%@ 0975-8887
%V 30
%N 1
%P 14-20
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper presents novel content based image retrieval (CBIR) methods using orthogonal wavelet transforms generated from 7 different transforms namely Walsh, Haar, Kekre, Slant, Hartley, DST and DCT. Here the feature vector size per image is greatly reduced by taking fractional coefficients of the transformed image. The feature vectors are extracted in fifteen different ways from the transformed image. Along with the first being all the coefficients of transformed image, fourteen reduced coefficients sets (as 50%, 25%, 12.5%, 6.25%, 3.125%, 1.5625% ,0.7813%, 0.39%, 0.195%, 0.097%, 0.048%, 0.024%, 0.012% and 0.006% of complete transformed image) are considered as feature vectors. Instead of using all coefficients of transformed images as the feature vector for image retrieval, these fourteen reduced coefficients sets are used, resulting into better performance and lower computations. The proposed CBIR techniques are implemented on a database having 1000 images spread across 11 categories. For each proposed CBIR technique 55 queries (randomly selected 5 images per category) are fired on the database and average precision and recall values are plotted to get precision-recall crossover point. The results have shown the performance improvement (higher precision-recall crossover point) with fractional coefficients compared to complete transform of image at reduced computations resulting in faster retrieval. The wavelet transform generated using Kekre transform for 0.048% reduced coefficient set gives the best performance among the proposed CBIR techniques.

References
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Index Terms

Computer Science
Information Sciences

Keywords

CBIR Fractional Coefficients Wavelet Transforms Walsh Haar Kekre Slant Hartley Sine Cosine

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