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Reseach Article

Graph Regularized Non-Negative Matrix Factorization for Image Retrieval

Published on April 2012 by Rani Mohanlal, P. Latha
International Conference in Recent trends in Computational Methods, Communication and Controls
Foundation of Computer Science USA
ICON3C - Number 7
April 2012
Authors: Rani Mohanlal, P. Latha
734fa741-8072-414e-93cc-3aae8e229c40

Rani Mohanlal, P. Latha . Graph Regularized Non-Negative Matrix Factorization for Image Retrieval. International Conference in Recent trends in Computational Methods, Communication and Controls. ICON3C, 7 (April 2012), 33-37.

@article{
author = { Rani Mohanlal, P. Latha },
title = { Graph Regularized Non-Negative Matrix Factorization for Image Retrieval },
journal = { International Conference in Recent trends in Computational Methods, Communication and Controls },
issue_date = { April 2012 },
volume = { ICON3C },
number = { 7 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 33-37 },
numpages = 5,
url = { /proceedings/icon3c/number7/6054-1055/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Recent trends in Computational Methods, Communication and Controls
%A Rani Mohanlal
%A P. Latha
%T Graph Regularized Non-Negative Matrix Factorization for Image Retrieval
%J International Conference in Recent trends in Computational Methods, Communication and Controls
%@ 0975-8887
%V ICON3C
%N 7
%P 33-37
%D 2012
%I International Journal of Computer Applications
Abstract

This paper presents a novel matrix factorization method for effectively performing the image retrieval in large image databases. Non-negative Matrix Factorization (NMF) provides a parts-based representation of data by finding two non-negative matrices whose product can well approximate the original data matrix. Although it has been applied successfully for several applications, simply using NMF for image retrieval provides low performance results which results from the fact that NMF fails to consider the geometric structure that is contained within the data. To solve the above problem, we encode the geometrical information contained in the data by constructing a nearest neighbor graph and perform matrix factorization based on this graph structure. In this work, 500 images from Corel dataset have been taken into consideration. We compared NMF and GNMF based method in the context of image retrieval. Experimental results demonstrate the effectiveness and robustness of GNMF based approach.

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Index Terms

Computer Science
Information Sciences

Keywords

Non-negative Matrix Factorization Local Invariance Assumption Graph Laplacian