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

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Presentation on theme: "Presented by Marlene Shehadeh Advanced Topics in Computer Vision ( 048921 ) Winter 2011-2012."— Presentation transcript:

1 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 Leibe ICCV 2011

5 Iconoid shift algorithm outline seeds Minimal spanning tree overlap distance pairwise distances medoid shift

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


11 Hinge kernel The kernel : Cuts off images with distance greater than the threshold


13 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.

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

15 results



18 Kernel effect

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

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

21 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

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

23 patter is assigned an indicator if the pattern belongs to the BCS Cut edge maximization Intra edge minimization

24 Reformulation as quadratic problem SDP is used to solve this problem

25 Similarity measure

26 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.

27 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

28 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

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

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

31 outline

32 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

33 Experimental results learning set of 500 images 17 categories of 100k initial images

34 FINDING ICONIC IMAGES BCSIconoid shift yes noPreliminary learning Single classMultiple classes input Single image(icon)K images (canonical base) Single image(icon)Representative set

35 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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