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Performance Comparison of Texture Pattern based Image Retrieval using Haar Transform with Binary and Ternary Image Maps

Published on None 2011 by H.B.Kekre, Sudeep D. Thepade, Varun K.Banura
journal_cover_thumbnail

International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET - Number 3
None 2011
Authors: H.B.Kekre, Sudeep D. Thepade, Varun K.Banura
2b21a54c-7286-4a35-a482-a337b7b4c6a2

H.B.Kekre, Sudeep D. Thepade, Varun K.Banura . Performance Comparison of Texture Pattern based Image Retrieval using Haar Transform with Binary and Ternary Image Maps. International Conference and Workshop on Emerging Trends in Technology. ICWET, 3 (None 2011), 23-28.

@article{
author = { H.B.Kekre, Sudeep D. Thepade, Varun K.Banura },
title = { Performance Comparison of Texture Pattern based Image Retrieval using Haar Transform with Binary and Ternary Image Maps },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { None 2011 },
volume = { ICWET },
number = { 3 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 23-28 },
numpages = 6,
url = { /proceedings/icwet/number3/2077-aca533/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A H.B.Kekre
%A Sudeep D. Thepade
%A Varun K.Banura
%T Performance Comparison of Texture Pattern based Image Retrieval using Haar Transform with Binary and Ternary Image Maps
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET
%N 3
%P 23-28
%D 2011
%I International Journal of Computer Applications
Abstract

The theme of the work presented here is performance comparison of texture pattern based image retrieval techniques using Haar transform with binary image maps and ternary image maps. Different texture patterns namely ‘4-pattern’, ‘16-pattern’, ‘64-pattern’ , ‘256-pattern’ and ‘1024-pattern’ are generated using Haar transform matrix and then compared with the two image maps binary and ternary (one a time) to generate the feature vector as the matching number of ones & minus ones (in case of binary image maps) and ones, zeros and minus ones (in case of ternary image maps) per texture pattern. The proposed content based image retrieval (CBIR) techniques are tested on a generic image database having 1000 images spread across 11 categories. For each proposed CBIR technique 55 queries (randomly selected 5 per category) are fired on the image database. To compare the performance of image retrieval techniques, crossover point of average precision and recall values of all the queries are computed per image retrieval technique. Ameliorated performance (higher precision and recall values) has been observed with the ternary image maps. Further the performance of proposed image retrieval methods is enhanced using the combination of original image and even image part. In the discussed image retrieval methods, the combination of original and even image part for 16-pattern texture with ternary image maps gives the highest crossover point of precision and recall reflecting better performance.

References
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  2. Dr. H.B.Kekre, Sudeep D. Thepade, Varun K. Banura, “Amelioration of Colour Averaging Based Image Retrieval Techniques using Even and Odd parts of Images”, International Journal of Engineering Science and Technology (IJEST), Volume 2, Issue 9, (ISSN: 0975-5462) Available online at http://www.ijest.info.
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Index Terms

Computer Science
Information Sciences

Keywords

CBIR Haar transform Texture Pattern Binary Ternary Image maps