Learning to Match Images in Large-Scale Collections

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.