Medoid shift For a given kernel and distance function Center is always a point in the set The clustering in performed iteratively
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
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
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.
Experimental results Dataset of 500k images of paris from tourist pictures Initial seed set is determined by Min-Hash Seed Generation
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
A Review of: Selecting Canonical Views for View- Based 3-D Object Recognition T. Denton et al ICPR 2004
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
Problem of maximizing weight edges is NP hard Approximate Solution: Semidefinite programming (SDP) Normalized cut
patter is assigned an indicator if the pattern belongs to the BCS Cut edge maximization Intra edge minimization
Reformulation as quadratic problem SDP is used to solve this problem
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.
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
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
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
A Review of: Finding Iconic Images Tamara L. Berg Alexander C. Berg CVPR’09 Internet Vision Workshop, 2009
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
Experimental results learning set of 500 images 17 categories of 100k initial images
FINDING ICONIC IMAGES BCSIconoid shift yes noPreliminary learning Single classMultiple classes input Single image(icon)K images (canonical base) Single image(icon)Representative set
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.