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Reseach Article

Content based Image Retrieval using Advanced Color and Texture Features

Published on March 2012 by Sagar Soman, Mitali Ghorpade, Vrushali Sonone, Satish Chavan
International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 9
March 2012
Authors: Sagar Soman, Mitali Ghorpade, Vrushali Sonone, Satish Chavan
0329b468-aa6c-4b14-918c-2f57e75c0737

Sagar Soman, Mitali Ghorpade, Vrushali Sonone, Satish Chavan . Content based Image Retrieval using Advanced Color and Texture Features. International Conference in Computational Intelligence. ICCIA, 9 (March 2012), 1-5.

@article{
author = { Sagar Soman, Mitali Ghorpade, Vrushali Sonone, Satish Chavan },
title = { Content based Image Retrieval using Advanced Color and Texture Features },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 9 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 1-5 },
numpages = 5,
url = { /proceedings/iccia/number9/5155-1067/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A Sagar Soman
%A Mitali Ghorpade
%A Vrushali Sonone
%A Satish Chavan
%T Content based Image Retrieval using Advanced Color and Texture Features
%J International Conference in Computational Intelligence
%@ 0975-8887
%V ICCIA
%N 9
%P 1-5
%D 2012
%I International Journal of Computer Applications
Abstract

The paper presents an efficient Content Based Image Retrieval (CBIR) system using color and texture. In proposed system, two different feature extraction techniques are employed. A universal content based image retrieval system uses color, texture and shape based feature extraction techniques for better matched images from the database. In proposed CBIR system, color and texture features are used. The texture features were extracted from the query image by applying block wise Discrete Cosine Transforms (DCT) on the entire image and from the retrieved images the color features were extracted by using moments of colors (Mean, Deviation and Skewness) theory. The proposed system has used Corel database of 1000 images. The feature vectors of the query image will then be compared with feature vectors of the database to obtain similar images. Individual and combined vectors using color and texture features were computed and the combined feature vector results were comparatively better. The proposed system provides an efficiency of 60%.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Content Based Image Retrieval Color Models DCT Color Moments