visualrank - applying pagerank to large-scale image search
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Intelligent Database Systems Lab
國立雲林科技大學National Yunlin University of Science and Technology
VisualRank- Applying PageRank to Large-Scale Image Search
Presenter: Chien-Hsing ChenAuthor: Yushi Jing Shumeet Baluja
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2008.PAMI (IEEE Transactions on Pattern Analysis and Machine Intelligence)
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Outline Motivation Objective Method Experiments Conclusion Comment
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Motivation retrieved images may be not fitting (satisfactory) or not diverse
The news shows a disappointed salesman of Coca Cola returns from his Middle East assignment. A friend asked, “Why weren’t you successful with the Arabs?”
How the image could be retrieved ?
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Example 2
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Objective improve quality of image retrieval by rearrange the results of
Google search engine incorrect retrieval
d80 Coca Cola
diversity (retrieved images should be different)
You should know:1. adjacency matrix, matrix product2. eigenvector, PageRank()
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Rearrange
previous worksQuery-to-images
in this paperImages-to-images
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Main idea 1/2
0.30.9
0.1
0.6
0.4
0.1
0.2
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Rearrange
Images-to-images1.
similar local features Web site source
my homepage V.S. Yahoo
2.
diversity
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Main idea 2/2
0.3 0.9
0.1
0.6
0.4
0.10.2
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Images-to-images 1/2
1 1 0 0 0
1 1 1 1 0
0 1 1 0 0
0 1 0 1 0
0 0 0 0 1
Top ranked images :
Adjacency matrixHow to connect between vertexes ?(how to build edge sets)
x1
x2
x3
x4
x5
x1 x2 x3 x4 x5
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Images-to-images 2/2
1 0.2 0.1 0.8 0.2
0.2 1 0.1 0.9 0.3
0.1 0.1 1 0.9 0.1
0.8 0.9 0.9 1 0.1
0.2 0.3 0.1 0.1 1Top ranked images :
Adjacency matrix How to give the scores between vertexes ?
x1
x2
x3
x4
x5
x1 x2 x3 x4 x5
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How to connect between vertexes ?Common local features 1/2
which pair has most number of common (similar) local features?
(a) local features, such as hands, eyes, are similar
local features are very different
(g)
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Common local features 2/2
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image relationship Which image has most number of common (similar) local features? A image of which features are similar to the features in the other images. The image is important.
1 0.6 0.8 0.6 0.1
0.6 1 0.5 0.4 0.2
0.8 0.5 1 0.7 0.1
0.5 0.4 0.7 1 0.1
0.1 0.2 0.1 0.1 1
× =
[n × n] matrix eigenvector
The entry is evaluated by “local features” uniform ?
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PageRank
3 8 9 11 100
PageRank() concerns the properties of “Hub” and “Authority”Web sites appearing in front of the Google responds are more important than that appearing in back of the ones.
d
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image diversity
Top ranked images with respect to diversity:
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Experiments
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c
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c
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Conclusion arrange the images from the results of Google search engine
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Comment Advantage
The aspect is novel and easy to implement. Drawback
less discussion in diversity Application
responds of search engine an option is to cluster the resulted images
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