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Reseach Article

Feature Extraction of Color Images using Sectorization of Discrete Sine Transform

Published on None 2011 by H.B.Kekre, Dhirendra Mishra
journal_cover_thumbnail
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET - Number 4
None 2011
Authors: H.B.Kekre, Dhirendra Mishra
7807a0b3-ae2d-42e2-81ec-71e137d54f5b

H.B.Kekre, Dhirendra Mishra . Feature Extraction of Color Images using Sectorization of Discrete Sine Transform. International Conference and Workshop on Emerging Trends in Technology. ICWET, 4 (None 2011), 27-32.

@article{
author = { H.B.Kekre, Dhirendra Mishra },
title = { Feature Extraction of Color Images using Sectorization of Discrete Sine Transform },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { None 2011 },
volume = { ICWET },
number = { 4 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 27-32 },
numpages = 6,
url = { /proceedings/icwet/number4/2082-algo69/ },
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 Dhirendra Mishra
%T Feature Extraction of Color Images using Sectorization of Discrete Sine Transform
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET
%N 4
%P 27-32
%D 2011
%I International Journal of Computer Applications
Abstract

A novel idea of sectorization has been applied on Full Discrete sine transformed (DST) images to extract the unique feature of images. The method of sectorization has been experimented over two newly generated planes i.e. Even and Odd planes out of full DST transformed images. These two planes sectored into 4,8,12 and 16 sectors in order to extract the efficient feature vectors. The process is applied in content based image retrieval to check it’s applicability. As CBIR needs the similarity measuring parameters for the similarity measures of all images with each other; we have used sum of absolute difference and the Euclidian distance as two parameters. The retrieval result of all sectors with respect to these two similarity measures are checked by means of LIRS,LSRR and average precision-recall cross over point plots. The proposed method works on the database consisting of 1055 images spread over 12 different classes.

References
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Index Terms

Computer Science
Information Sciences

Keywords

CBIR DST Euclidian Distance Sum of Absolute Difference Precision and Recall LIRS LSRR