Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
A bootstrapping approach to annotating large image collection
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
Towards Scalable Dataset Construction: An Active Learning Approach
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Weakly Supervised Object Localization with Stable Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Scene Segmentation Using the Wisdom of Crowds
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
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Millions of place-specified photos are uploaded on the internet. Modeling and representing the landmark is very important for landmark retrieval and auto-annotation. In this paper, we aim at collecting images with a specific landmark and labeling the landmark in the images. We propose a weakly supervised labeling approach based on both textual and visual features. Firstly, we cluster the raw landmark dataset with noisy annotation according to the visual context, and obtain a summary of the landmark. Secondly, we mine the local features according to the confidence values of key points and locate the initial region of the landmark. Thirdly, we use Multiple Instance Learning to label the landmark. The contribution of this paper is to automatically learn a landmark representation from the searched data based on a weakly supervised learning approach. We implement our approach on "Statue of Liberty", "Bird Nest" and "Notre Dame de Paris". The experimental results demonstrate that our approach is promising.