Research Article
Image Re-ranking using Information Gain and Relative Consistency through Multi-graph Learning
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 147 - Issue 8 |
| Published: Aug 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 },
year = { 2016 },
volume = { 147 },
number = { 8 },
pages = { 29-32 },
doi = { 10.5120/ijca2016911131 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A Poonam N. Borase
%A Supriya A. Kinariwala
%T Image Re-ranking using Information Gain and Relative Consistency through Multi-graph Learning%T
%J International Journal of Computer Applications
%V 147
%N 8
%P 29-32
%R 10.5120/ijca2016911131
%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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