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Image Re-ranking using Information Gain and Relative Consistency through Multi-graph Learning

by Poonam N. Borase, Supriya A. Kinariwala
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 147 - Number 8
Year of Publication: 2016
Authors: Poonam N. Borase, Supriya A. Kinariwala
10.5120/ijca2016911131

Poonam N. Borase, Supriya A. Kinariwala . Image Re-ranking using Information Gain and Relative Consistency through Multi-graph Learning. International Journal of Computer Applications. 147, 8 ( Aug 2016), 29-32. DOI=10.5120/ijca2016911131

@article{ 10.5120/ijca2016911131,
author = { Poonam N. Borase, Supriya A. Kinariwala },
title = { Image Re-ranking using Information Gain and Relative Consistency through Multi-graph Learning },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 147 },
number = { 8 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume147/number8/25675-2016911131/ },
doi = { 10.5120/ijca2016911131 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:51:22.497959+05:30
%A Poonam N. Borase
%A Supriya A. Kinariwala
%T Image Re-ranking using Information Gain and Relative Consistency through Multi-graph Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 147
%N 8
%P 29-32
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

After receiving a lot of attention towards text based searching for image retrieval, researchers have focused on content based image retrieval. Visual re-ranking is a method of image retrieval, which has been widely accepted to boost the accuracy of traditional text-based image retrieval. Current trend of this method is to combine the retrieval results from various visual features to boost the overall performance. The challenge in this trend of re-ranking is to exploit the complementary property of different features effectively. Our purpose basically comes under feature based image retrieval on three different modalities, so that retrieval re-ranking will be more accurate and effective. We deal with mainly two terms: information gain and relative ranking consistency among multiple modalities. Our submodular re-ranking framework can be easily used in re-ranking problems for real-time search engines.

References
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  2. Wang, M., Li, H., Tao, D., Lu, K., Wu, X.: Multimodal graph-based re-ranking for web image search. IEEE Transactions on Image Processing 21 (2012) 4649-4661
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  8. https://www.quora.com/How-does-features-extraction-on-images-work
  9. R. Gonzales, R. E. Woods, “Digital Image Processing,” 2nd Ed.
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Index Terms

Computer Science
Information Sciences

Keywords

Information gain Relative Consistency Graph Construction Re-ranking.

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