TY - GEN
T1 - Modeling and recognition of landmark image collections using iconic scene graphs
AU - Li, Xiaowei
AU - Wu, Changchang
AU - Zach, Christopher
AU - Lazebnik, Svetlana
AU - Frahm, Jan Michael
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-540-88682-2_33
DO - 10.1007/978-3-540-88682-2_33
M3 - Conference contribution
AN - SCOPUS:56749152232
SN - 3540886818
SN - 9783540886815
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 427
EP - 440
BT - Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
PB - Springer
T2 - 10th European Conference on Computer Vision, ECCV 2008
Y2 - 12 October 2008 through 18 October 2008
ER -