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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

Iconoid shift algorithm outline
Iconoid shift algorithm outline

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

Normalized cut
Normalized cut

  • patter is assigned an indicator

    • if the pattern belongs to the BCS

  • Cut edge maximization

  • Intra edge minimization

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.

Future work
Future work canonical view may not be top ranked

  • 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

EVALUATION canonical view may not be top ranked

  • 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: canonical view may not be top rankedFinding Iconic Images Tamara L. Berg Alexander C. Berg CVPR’09Internet Vision Workshop, 2009

outline canonical view may not be top ranked

Ranking canonical view may not be top ranked

  • 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 canonical view may not be top ranked

  • learning set of 500 images

  • 17 categories of 100k initial images

References canonical view may not be top ranked

  • 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.