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Reseach Article

Visual Search Optimization Using Concept Related Re-Ranking

Published on April 2012 by G. Lakshmi Narayanan, V. Kalaivani
International Conference in Recent trends in Computational Methods, Communication and Controls
Foundation of Computer Science USA
ICON3C - Number 1
April 2012
Authors: G. Lakshmi Narayanan, V. Kalaivani
b3cdf539-711e-41ea-a424-c91d3ca53eab

G. Lakshmi Narayanan, V. Kalaivani . Visual Search Optimization Using Concept Related Re-Ranking. International Conference in Recent trends in Computational Methods, Communication and Controls. ICON3C, 1 (April 2012), 28-32.

@article{
author = { G. Lakshmi Narayanan, V. Kalaivani },
title = { Visual Search Optimization Using Concept Related Re-Ranking },
journal = { International Conference in Recent trends in Computational Methods, Communication and Controls },
issue_date = { April 2012 },
volume = { ICON3C },
number = { 1 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 28-32 },
numpages = 5,
url = { /proceedings/icon3c/number1/6005-1006/ },
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 G. Lakshmi Narayanan
%A V. Kalaivani
%T Visual Search Optimization Using Concept Related Re-Ranking
%J International Conference in Recent trends in Computational Methods, Communication and Controls
%@ 0975-8887
%V ICON3C
%N 1
%P 28-32
%D 2012
%I International Journal of Computer Applications
Abstract

Visual search re-ranking defined as re-ordering visual documents like image, videos etc. based on the initial search. Ranking the multimedia content like images, videos are a challenging research topic in the noisy visual environment. Now days, leading search engines are fully depends on the description, title, surrounding information of an image which produce irrelevant image which are not equal to visual content. In this paper, a new approach proposed to improve the visual search precision level. First, the initial ranking occurred based on the textual information like tag, description relevancy which didn't produce relevant images. Second, by using visual query examples in the search engine to filter the images based on feature. The visual equivalence between the images calculated to increase the relevance results. Mainly the Equivalence Re-ranking approach focused on the relationship between the concepts of documents considered to reorder the initial search result with higher resolution images for optimizing the list of images. And by avoiding and removing irrelevant image along with the low resolution images by re-ranking approach, will increase the performance of search engine.

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Index Terms

Computer Science
Information Sciences

Keywords

Pair-wise Learning Search Re-ranking Visual Search Example Re-ranking Ep Re-ranking Correlation