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View Selection

View Selection. Presented by Marlene Shehadeh . Advanced Topics in Computer Vision ( 048921 ) . Winter 2011-2012. Problem. Goals . arrange: Icon BCS . A Review of: Discovering Favorite Views of Popular Places with Iconoid Shift Tobias Weyand and Bastian Leibe ICCV 2011.

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View Selection

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  1. View Selection Presented by Marlene Shehadeh Advanced Topics in Computer Vision (048921) Winter 2011-2012

  2. Problem

  3. Goals • arrange: • Icon • BCS

  4. A Review of:Discovering Favorite Views of Popular Places with Iconoid Shift Tobias Weyand and Bastian LeibeICCV 2011

  5. Iconoid shift algorithm outline

  6. Medoid shift • For a given kernel and distance function • Center is always a point in the set • The clustering in performed iteratively

  7. Homography Overlap Distance • Modes search for image with maximal overlap with its neighbors • The distance measure must: • Reward similar views • Penalize different view ( zoom, pan) • Consider the overlap region • are the areas of images and bounding boxes around the inliers

  8. Homeography distance

  9. Transitive Homography Overlap Distance • Medoid shift requires the distance of each pair of images in the set • Direct feature matching distance calculation is very expensive • Solution: • Represent local neighborhood by a tree • Compute distance along edges • Calculate the distance using the tree path

  10. Hinge kernel • The kernel : • Cuts off images with distance greater than the threshold

  11. Implementation efficiency • Local Exploration and Minimum Spanning Tree Construction • node i stores overlap region with the root • homography overlap distances to root are computed by propagating the overlap region • only O(N) propagation steps have to be performed • Homography Overlap Propagation (HOP) • For parent i the homography overlap is propagated to all nodes j in its subtree • the transitive propagation scheme is used to compute the distances between all nodes n and m that have common parent i • O(N) memory complexity, O(N2) time complexity.

  12. Experimental results • Dataset of 500k images of paris from tourist pictures • Initial seed set is determined by Min-Hash Seed Generation

  13. results

  14. Kernel effect

  15. EVALUATION • Experimental evaluation • Many visual results • Comparison to existing methods • Subsystem tests • Large random dataset Novel approach in image soft clustering Novel combination of existing parts Well written Not self contained Technically convincing

  16. A Review of:Selecting Canonical Views for View-Based 3-D Object Recognition T. Denton et alICPR 2004

  17. algorithm outline • Given a set of views P and a similarity function S • Construct a graph: • Views are vertices • Edges are proportional to nodes similarity • Find bounded canonical subset(BCS) • Minimize the sum of edges within BCS • Maximize the sum of edges between BCS and the rest of the set

  18. Problem of maximizing weight edges is NP hard • Approximate Solution: • Semidefinite programming (SDP) • Normalized cut

  19. Normalized cut • patter is assigned an indicator • if the pattern belongs to the BCS • Cut edge maximization • Intra edge minimization

  20. Reformulation as quadratic problem • SDP is used to solve this problem

  21. Similarity measure

  22. Experimantal results • 2D images were acquired from 3D synthetic objects • Each object has 19 views acquired by sampeling the view sphere • The resulting BCS views were compared to the rest of the set and ranked. • In 90.6% of the cases the correct canonical view was among the top 6 ranks.

  23. when different objects share similar views the correct canonical view may not be top ranked • If the bounds are set too low for a complex object, whole classes of object views are not represented in the object’s BCS

  24. Future work • Evaluate the method quantitivly • Study the effect of set size and boundaries on performance • Adjust the algorithm to use a simpler matching method to replace the many to many complex method used

  25. EVALUATION • Experimental evaluation • Synthetic results only • No comparison to other methods • No quantative results Extention to previous works for summarizing sets Novel combination of existing parts Not self contained

  26. A Review of:Finding Iconic Images Tamara L. Berg Alexander C. Berg CVPR’09Internet Vision Workshop, 2009

  27. outline

  28. Ranking • Learn model • The image consists of a rectangular foreground, and background • Possible layouts are examined • High score is give to an image with icon layout • Top ranked images are are used to calculate similarity

  29. Experimental results • learning set of 500 images • 17 categories of 100k initial images

  30. References • T. Weyand and B. Leibe ,”Discovering Favorite Views of Popular Places with Iconoid Shift”, ICCV 2011. • T. Denton, M. Demirci, J. Abrahamson, A. Shokoufandeh,and S. Dickinson. “Selecting Canonical Views for View-based 3D Object Recognition”. In ICPR, 2004. • T. Berg and A. B. Berg. “Finding Iconic Images”. In CVPR’09,Internet Vision Workshop, 2009. • O. Chum and J. Matas. “Large-scale discovery of spatiallyrelated images”. In PAMI, 2010.

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