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VisualRank : Applying PageRank to Large-Scale Image Search

VisualRank : Applying PageRank to Large-Scale Image Search. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, NOVEMBER 2008. Yushi Jing, Member, IEEE Shumeet Baluja , Member, IEEE.

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VisualRank : Applying PageRank to Large-Scale Image Search

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  1. VisualRank: Applying PageRank to Large-Scale Image Search IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, NOVEMBER 2008 Yushi Jing, Member, IEEE ShumeetBaluja, Member, IEEE [24] Y. Jing, S. Baluja, and H. Rowley, “Canonical Image Selection from the Web,” Proc. Sixth Int’l Conf. Image and Video Retrieval, pp. 280-287, 2007.

  2. Outline • Introduction • Similarity graph[24] • PageRank & VisualRank • Hashing • Experiments • Conclusion

  3. Outline • Introduction • Similarity graph[24] • PageRank & VisualRank • Hashing • Experiments • Conclusion

  4. Search for “d80” & “coca cola” by traditional search engine

  5. Introduction • Visual theme, ex: “coca cola” logo • CBIR:content-based image retrieval • Pure • Composite • “Visual-filter” via Probabilistic Graphical Models(PGMs)[7] • Compare: • Object category learner • image search engine [7] R. Fergus, P. Perona, and A. Zisserman, “A Visual Category Filter for Google Images,” Proc. Eighth European Conf. Computer Vision, pp. 242-256, 2004.

  6. Introduction

  7. Introduction • Combine[24] • pairwise visual similarity among images • nonvisual signals • VisualRank • Based on PageRank • Large number of queries & images • Goal • More accurate search ranking

  8. introducton

  9. Outline • Introduction • Similarity graph[24] • PageRank & VisualRank • Hashing • Experiments • Conclusion

  10. Features generation • Local descriptor • SIFT&compare[29] [29] K. Mikolajczyk and C. Schmid, “A Performance Evaluation of Local Descriptors,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, Oct. 2005.

  11. Similarity graph • pairwise

  12. Similarity graph • Top 1000results of “Mona-lisa”

  13. Outline • Introduction • Similarity graph[24] • PageRank & VisualRank • Hashing • Experiments • Conclusion

  14. PageRank • Conception • Vote • eigenvector centrality A B D C PR(A) = PR(B) + PR(C) + PR(D)

  15. PageRank A B C D

  16. PageRank q=0.15 Random walk

  17. PageRank • Markov matrix

  18. VisualRank usually d>0.8

  19. Link spam • Well connected image V.S. VisualRank, “Nemo”

  20. Outline • Introduction • Similarity graph[24] • PageRank & VisualRank • Hashing • Experiments • Conclusion

  21. Matching • Precluster • “Paris”, “Eiffel Tower”, and “Arc de Triomphe” • Top-N, and compute VisualRank • Hashing • Locality Sensitive Hashing (LSH) • Feature descriptor as the key

  22. Locality Sensitive Hashing (LSH) • An approximate k-NN technique • Hash function: • a is d-dimensional random vector • b is real number from range • W defines the quantization of the features • V is the original feature vector

  23. Flow(1/3) • Resize 500*500 pix, 1000 web images 3000,000 to 700,000 feature vectores • L hash table H=H1, H2,…,HL, each with K hash functions, L=40, W=100, K=3

  24. Flow(2/3) • Matched descriptor • Have same key more than C=3 hash table • Hough Transform

  25. Flow(3/3) • Similarity • Matched images • More than 3 features • no. of matches divide by their avg. number of local features • Given similarity matrix S, and use VisualRank

  26. Outline • Introduction • Similarity graph[24] • PageRank & VisualRank • Hashing • Experiments • Conclusion

  27. Experiments • 2,000 most popular product queries on Google, ex: “ipod”, “Xbox” • the top 1,000 search results each query in July 2007 Google • Filter • Fewer than 5% images at least one connection • Remaining 1,000 queries

  28. Experiment 1 • Evaluate • “irrelevancy” of our ranking • Mixed Top 10 VisualRank & top 10 googleRemove duplicates and ask “which are least relevant?” • Ask 150 evaluators, randomly 50 queries

  29. Experiment 2 • VisualRankbias, • pT=VjT=[1/m, …, 1/m, 0, …, 0] • HeuristicRank– a pure CBIR system j

  30. Experiment 3 • Collected 40 top images each click numbers from google • Compare • Sum of VisualRank top 20 click numbers • Sum of default ranking top 20 click numbers • VisualRank exceeds 17.5% than default Google ranking

  31. Landmarks • 80 common landmark, ex: “Eiffel Tower,”“Big Ben,” “Coliseum,” and “Lincoln Memorial.”

  32. Outline • Introduction • Similarity graph[24] • PageRank & VisualRank • Hashing • Experiments • Conclusion

  33. Conclusion • VisualRank applying PageRank conception and combined • Default Google ranking • similarity graph between images • VisualRank can outperform the default Google on the vast majority of queries • Reduce the number of irrelevant images efficiently

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