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Reseach Article

Empirical Evaluation of Dissimilarity Measures for Content-based Image Retrieval

by Marziyeh Hojati, Mehdi Rezaeian
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 11
Year of Publication: 2014
Authors: Marziyeh Hojati, Mehdi Rezaeian
10.5120/18418-9573

Marziyeh Hojati, Mehdi Rezaeian . Empirical Evaluation of Dissimilarity Measures for Content-based Image Retrieval. International Journal of Computer Applications. 105, 11 ( November 2014), 1-7. DOI=10.5120/18418-9573

@article{ 10.5120/18418-9573,
author = { Marziyeh Hojati, Mehdi Rezaeian },
title = { Empirical Evaluation of Dissimilarity Measures for Content-based Image Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 11 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number11/18418-9573/ },
doi = { 10.5120/18418-9573 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:25.369970+05:30
%A Marziyeh Hojati
%A Mehdi Rezaeian
%T Empirical Evaluation of Dissimilarity Measures for Content-based Image Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 11
%P 1-7
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the performance of various distances in image retrieval and image classification is evaluated based on color and texture features. The evaluation has been done in two classification: k-nearest neighbors and support vector machine(SVM). Given SVM classification is a learning system and any learning system is susceptible to error, therefore in this study a method is proposed for the user interaction. In this method if an error occurs in the first implementation of SVM classification or an image is displayed incorrectly, the next executions show similar images or to inform the user that the image is not in the database. The results of the experiment will be presented and investigated based on color histogram, color moment, color correlogram, gabor features, local binary pattern and wavelet transform in a database.

References
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Index Terms

Computer Science
Information Sciences

Keywords

content-based image retrieval color feature texture feature support vector machine k-nearest neighbors.