Matching and Predicting Street Level Images

The paradigm of matching images to a very large dataset has been used for numerous vision tasks and is a powerful one. If the image dataset is large enough, one can expect to find good matches of almost any image to the database, allowing label transfer [3, 15], and image editing or enhancement [6, 11]. Users of this approach will want to know how many images are required, and what features to use for finding semantic relevant matches. Furthermore, for navigation tasks or to exploit context, users will want to know the predictive quality of the dataset: can we predict the image that would be seen under changes in camera position? We address these questions in detail for one category of images: street level views. We have a dataset of images taken from an enumeration of positions and viewpoints within Pittsburgh. We evaluate how well we can match those images, using images from non-Pittsburgh cities, and how well we can predict the images that would be seen under changes in camera position. We compare performance for these tasks for eight different feature sets, finding a feature set that outperforms the others (HOG). A combination of all the features performs better in the prediction task than any individual feature. We used Amazon Mechanical Turk workers to rank the matches and predictions of different algorithm conditions by comparing each one to the selection of a random image. This approach can evaluate the efficacy of different feature sets and parameter settings for the matching paradigm with other image categories.