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Proposed Image Similarity Measurement Model based on Hypergraph

by A. E. A. Elaraby, Alain Bretto
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 21
Year of Publication: 2014
Authors: A. E. A. Elaraby, Alain Bretto
10.5120/16918-6543

A. E. A. Elaraby, Alain Bretto . Proposed Image Similarity Measurement Model based on Hypergraph. International Journal of Computer Applications. 96, 21 ( June 2014), 41-43. DOI=10.5120/16918-6543

@article{ 10.5120/16918-6543,
author = { A. E. A. Elaraby, Alain Bretto },
title = { Proposed Image Similarity Measurement Model based on Hypergraph },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 21 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 41-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume96/number21/16918-6543/ },
doi = { 10.5120/16918-6543 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:22:22.489918+05:30
%A A. E. A. Elaraby
%A Alain Bretto
%T Proposed Image Similarity Measurement Model based on Hypergraph
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 21
%P 41-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this article, we propose a new image similarity measurement model (SMM) based hypergraph which is easy to calculate and applicable to various image processing application. Hypergraphs are now used in many domains such as chemistry, engineering and image processing. We present an overview of a hypergraph-based Image representation and the Image Adaptive Neighborhood Hypergraph (IANH). With the IANH it is possible to build a new powerful similarity measurement model. Although the new model is mathematically defined and no human visual system model is explicitly employed, our experiments on various image distortion types indicate the efficient of proposed model.

References
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  2. A. Bretto. Introduction to Hypergraph Theory and its Applications to Image Processing. Mongraphy in: Advances in Imaging and Electron Physics. Academic Press Elsevier, vol. 131, march 2004.
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  6. S. Rital, A. Bretto, H. Cherifi and D. Aboutajdine. Application of Adaptive Hypergraph Model to Impulsive Noise Detection. CAIP'2001, Springer-Verlag, 2001, Warsaw, Poland, September 5-7, Lecture Note in Computer Sciences, 34-42.
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  10. T. Chen and H. R. Wu. Application of Partition-Based Median Type Filters for Suppressing Noise in Images. IEEE Transactions on Image Processing, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Image Similarity Image Processing Similarity Measurement Model .

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