Image re ranking system

An Approach to Re-Rank Retrieved Images - Image Search Results with Visual

Similarity

Presented by

Veningston .KM.E student, Dept of CSE,Anna University – Coimbatore,

India.

Guided by

Mr. M. Newlin RajkumarLecturer, Dept of CSE,

Anna University – Coimbatore, India.

04/11/23 Department of Computer Science and Engineering

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Objective

To address a ranking problem in web image retrieval

System to re-rank images returned by image search engine

Re-ranking images by incorporating,visual aspects visual similarity


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Introduction

Image Retrieval System Comparative study on text & image based search Interest point extraction - visual content of images Re-ranking the results of text based systems

using visual information Finding the largest set of most similar images Rearranging images based on the similarity


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Why image Re-ranking?

To maximize relevancy of image results To achieve diversity of image results


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Proposed Scheme

Goal Retrieve image results that are relevant Finding common features among images

Overview Interest points on the images are extracted Similarity of each pair of images are computed Generate graph model Apply page ranking


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Finding common features among images

Similarity measurement to handle potential rotation, scale and perspective transformations.


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Scale Invariant Feature Transform Image matching and features to similarity


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Image similarity

Given two images u and v, Corresponding descriptor vector, Du = (d1

u, d2

u, ...dmu ) and Dv = (d1

v, d2v, ...dn

v ), Define the similarity between two images

simply as the number of interest points shared between two images divided by their average number of interest points.


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Graph model

Given the visual similarities of the images to be ranked. Treat images as web documents Treat similarities as visual hyperlinks Estimate the probability of images being visited by

a user // using page rank Images with more estimated visits are ranked

higher


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Similarity to Centrality

Centrality – importance of images Given a graph with vertices and a set of

weighted edges, define and measure the “importance” of each of the vertices

Vertices = images Egde weights = similarity

A vertex closer to an important vertex should rank higher than others


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Similarity to Centrality Ranking scores correspond to probability of

arriving in each vertex by traversing through the graph

Adjacency matrix for unweighted graph

Matrix constructed from the weights of the edges in the

graph

Decision to take a particular path defined by

weighted edges


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Page Ranking (PR)

Determines the order of search results

Method of measuring a page’s importance

Results are based on this priority order

AA BB

CC

Search result Priority

B 3

A 2

C 1

D 1

E 1

DDEE


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Overall scheme

Computationally infeasible to compute similarities for all images indexed by search engine

Pre-cluster web images based on metadata Define the similarity of images Given a query, extract top - N results

returned, create graph of visual similarity on the N images

Compute image rank only on this subset


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Performance metrics

Precision and recall

number of relevant images in the returned images

Recall = ----------------------------------------------------------------------

total number of relevant images in the database

number of relevant images in the returned images

Precision = ---------------------------------------------------------------------

total number of returned images

Result analysis Screen shots


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Merits

Minimizing irrelevant images Selecting small set of images Computational cost


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Conclusion

Ability to reduce the number of irrelevant images

A tiny set of important images can be selected from a very large set of candidates


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Future work

Extensions of this technique to a query driven feature selection.


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References

[1] Yushi Jing, Shumeet Baluja. VisualRank: Applying PageRank to Large-Scale Image Search. IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol 30, No. 11:1887–1890, 2008.

[2] R. Datta, D. Joshi, J. Li, and J. Wang. Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys, vol. 40, no. 2, 2008.

[3] Yushi Jing, Shumeet Baluja. PageRank for Product Image Search. WWW 2008, April 21–25, 2008, Beijing, China. ACM International conf. 2008.

[4] K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Transaction on Pattern Analysis and Machine Intelligence, 27(10):1615–1630, 2005.

[5] S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 24(24):509–522, 2002.


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Thank you for your Attention

Image re ranking system

Education