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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

View Selection

Presented by

Marlene Shehadeh

Advanced Topics in Computer Vision (048921)

Winter 2011-2012

  • arrange:
  • Icon
  • BCS
A Review of:Discovering Favorite Views of Popular Places with Iconoid Shift Tobias Weyand and Bastian LeibeICCV 2011
medoid shift
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
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
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
hinge kernel
Hinge kernel
  • The kernel :
  • Cuts off images with distance greater than the threshold
implementation efficiency
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
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

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 alICPR 2004
algorithm outline
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
normalized cut
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
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
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

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’09Internet Vision Workshop, 2009
  • 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 results1
Experimental results
  • learning set of 500 images
  • 17 categories of 100k initial images
  • 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.