Modeling and recognition of landmark image collections using iconic scene graphs

Xiaowei Li, Changchang Wu, Christopher Zach, Svetlana Lazebnik, Jan Michael Frahm

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper presents an approach for modeling landmark sites such as the Statue of Liberty based on large-scale contaminated image collections gathered from the Internet. Our system combines 2D appearance and 3D geometric constraints to efficiently extract scene summaries, build 3D models, and recognize instances of the landmark in new test images. We start by clustering images using low-dimensional global "gist" descriptors. Next, we perform geometric verification to retain only the clusters whose images share a common 3D structure. Each valid cluster is then represented by a single iconic view, and geometric relationships between iconic views are captured by an iconic scene graph. In addition to serving as a compact scene summary, this graph is used to guide structure from motion to efficiently produce 3D models of the different aspects of the landmark. The set of iconic images is also used for recognition, i.e., determining whether new test images contain the landmark. Results on three data sets consisting of tens of thousands of images demonstrate the potential of the proposed approach.

Original languageEnglish (US)
Title of host publicationComputer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
Number of pages14
EditionPART 1
ISBN (Print)3540886818, 9783540886815
StatePublished - 2008
Externally publishedYes
Event10th European Conference on Computer Vision, ECCV 2008 - Marseille, France
Duration: Oct 12 2008Oct 18 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5302 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th European Conference on Computer Vision, ECCV 2008

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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