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Spatial Random Partition for Common Visual Pattern Discovery

ICCV 2007. Spatial Random Partition for Common Visual Pattern Discovery. Junsong Yuan and Ying Wu EECS Dept. Northwestern Univ. {j-yuan,yingwu}@northwestern.edu. The Problem. Can you find common posters in the two images?. Challenges. No prior knowledge of the common patterns

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Spatial Random Partition for Common Visual Pattern Discovery

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  1. ICCV 2007 Spatial Random Partition for Common Visual Pattern Discovery Junsong Yuan and Ying Wu EECS Dept. Northwestern Univ. {j-yuan,yingwu}@northwestern.edu CVPR 2006 New York City

  2. The Problem • Can you find common posters in the two images? ICCV 2007 Rio de Janeiro, Brazil

  3. Challenges • No prior knowledge of the common patterns • What are they ? appearances • Where are they ? locations • How large are they ? scales • How many of them ? number of instances • Computationally demanding • Exponentially large solution space • Large image dataset • Robust similarity matching ICCV 2007 Rio de Janeiro, Brazil

  4. Related Work • Pattern Discovery by Matching Visual Words • J. Sivic and A. Zisserman, CVPR04 • J. Yuan, Y. Wu and M.Yang, CVPR07 • S. Nowozin, K. Tsuda, T. Uno, T. Kudo and G. Bakir, CVPR07 • T. Quack, V. Ferrari, B. Leibe and L. V. Gool, ICCV07 • … • Pattern Discovery by Direct Matching • O. Boiman and M. Irani, ICCV05, NIPS06 • K. Grauman and T. Darrell, CVPR06, NIPS07 • K.-K. Tan and C.-W. Ngo, ICCV05 • N. Ahuja and S. Todorovic, CVPR06, ICCV07 • … ICCV 2007 Rio de Janeiro, Brazil

  5. Spatial Random Partition ICCV 2007 Rio de Janeiro, Brazil

  6. Visual Primitives • Visual Primitives: Scale Invariant Feature Transformation (SIFT, D.Lowe, IJCV04 ) • Locality Sensitive Hashing (LSH) for matching visual primitives • For each visual primitive, search for its matches from other images, based on Euclidean distance ICCV 2007 Rio de Janeiro, Brazil

  7. Matching Subimages • A many-to-many assignment problem. • Fast approximation by set intersection: • where is the # of visual primitives in the subimage ICCV 2007 Rio de Janeiro, Brazil

  8. Another View: Max Flow Visual primitives subimage Problem: Matching two sets of m and n points (feature vectors) Fast Approximate Solution: set intersection (linear complexity) ICCV 2007 Rio de Janeiro, Brazil

  9. An Example • Final estimation of similarity score: = 3 ICCV 2007 Rio de Janeiro, Brazil

  10. Voting for Common Patterns ICCV 2007 Rio de Janeiro, Brazil

  11. Asymptotic Property • Theorem: Given two pixel i and j, where i locates in a common pattern while j locates in the background, let and the total votes i and j receives regarding to K random partitions. Both and are discrete random variables and we have • Proof: using the weak law of large numbers, see the Appendix for details ICCV 2007 Rio de Janeiro, Brazil

  12. Localization of Common Patterns ICCV 2007 Rio de Janeiro, Brazil

  13. Various Number of Partitions ICCV 2007 Rio de Janeiro, Brazil

  14. ICCV 2007 Rio de Janeiro, Brazil

  15. Image Irregularity Detection • Differences from common pattern discovery • disocver unpopular subimages instead of popular ones • Adjust voting weight proportional to the subimage size: the larger the unpopular subimage, the more possible it contains an irregular pattern ICCV 2007 Rio de Janeiro, Brazil

  16. Evaluation • Collect 8 image datasets, each contains 4-8 images. An image dataset contains 1-3 common patterns each has 2-4 instances (* indicates the dataset containing multiple common patterns ) • Comparisons of computational complexity, around 12 sec. for 2 images J. Sivic & A. Zisserman 04 O. Boiman & M. Irani 05 ICCV 2007 Rio de Janeiro, Brazil

  17. Conclusion • A novel spatial random partition method for common pattern discovery and irregularity detection in images • No construction of visual vocabularies • Trade-off of performance and efficiency by the total number of random partitions • Efficient by using LSH and approximate matching between subimages • Theoretically justified by the asymptotic property of the algorithm ICCV 2007 Rio de Janeiro, Brazil

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