visualrank - applying pagerank to large-scale image search

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

visualrank - applying pagerank to large-scale image search

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