Re-ranking of web image search results using a graph algorithm

We propose a method to improve the results of image search engines on the Internet to satisfy users who desire to see relevant images in the first few pages. The method re-ranks the results of text based systems by incorporating visual similarity of the resulting images. We observe that, together wi...

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Published in2008 19th International Conference on Pattern Recognition pp. 1 - 4
Main Authors Zitouni, H., Sevil, S., Ozkan, D., Duygulu, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2008
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ISBN9781424421749
1424421748
ISSN1051-4651
DOI10.1109/ICPR.2008.4761472

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Abstract We propose a method to improve the results of image search engines on the Internet to satisfy users who desire to see relevant images in the first few pages. The method re-ranks the results of text based systems by incorporating visual similarity of the resulting images. We observe that, together with many unrelated ones, results of text based systems include a subset of correct images, and this set is, in general, the largest one which has the most similar images compared to other possible subsets. Based on this observation, we present similarities of all images in a graph structure, and find the densest component that corresponds to the largest set of most similar subset of images. Then, to re-rank the results, we give higher priority to the images in the densest component, and rank the others based on their similarities to the images in the densest component. The experiments are carried out on 18 category of images from.
AbstractList We propose a method to improve the results of image search engines on the Internet to satisfy users who desire to see relevant images in the first few pages. The method re-ranks the results of text based systems by incorporating visual similarity of the resulting images. We observe that, together with many unrelated ones, results of text based systems include a subset of correct images, and this set is, in general, the largest one which has the most similar images compared to other possible subsets. Based on this observation, we present similarities of all images in a graph structure, and find the densest component that corresponds to the largest set of most similar subset of images. Then, to re-rank the results, we give higher priority to the images in the densest component, and rank the others based on their similarities to the images in the densest component. The experiments are carried out on 18 category of images from.
Author Ozkan, D.
Duygulu, P.
Zitouni, H.
Sevil, S.
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Snippet We propose a method to improve the results of image search engines on the Internet to satisfy users who desire to see relevant images in the first few pages....
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SubjectTerms Computer vision
Filters
Image databases
Image recognition
Image segmentation
Instruments
Internet
Layout
Search engines
Title Re-ranking of web image search results using a graph algorithm
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