Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Proceedings of the 18th ACM conference on Information and knowledge management
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Avoiding confusing features in place recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Building Rome on a cloudless day
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Descriptor learning for efficient retrieval
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Geometric Latent Dirichlet Allocation on a Matching Graph for Large-scale Image Datasets
International Journal of Computer Vision
Ensemble of exemplar-SVMs for object detection and beyond
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Informative feature selection for object recognition via Sparse PCA
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Many computer vision applications require computing structure and feature correspondence across a large, unorganized image collection. This is a computationally expensive process, because the graph of matching image pairs is unknown in advance, and so methods for quickly and accurately predicting which of the O(n2) pairs of images match are critical. Image comparison methods such as bag-of-words models or global features are often used to predict similar pairs, but can be very noisy. In this paper, we propose a new image matching method that uses discriminative learning techniques--applied to training data gathered automatically during the image matching process--to gradually compute a better similarity measure for predicting whether two images in a given collection overlap. By using such a learned similarity measure, our algorithm can select image pairs that are more likely to match for performing further feature matching and geometric verification, improving the overall efficiency of the matching process. Our approach processes a set of images in an iterative manner, alternately performing pairwise feature matching and learning an improved similarity measure. Our experiments show that our learned measures can significantly improve match prediction over the standard tf-idf-weighted similarity and more recent unsupervised techniques even with small amounts of training data, and can improve the overall speed of the image matching process by more than a factor of two.