Learning Search Path for Region-level Image Matching

Finding a region of an image which matches to a query from a large number of candidates is a fundamental problem in image processing. The exhaustive nature of the sliding window approach has encouraged works that can reduce the run time by skipping unnecessary windows or pixels that do not play a substantial role in search results. However, such a pruning-based approach still needs to evaluate the non-ignorable number of candidates, which leads to a limited efficiency improvement. We propose an approach to learn efficient search paths from data. Our model is based on a CNN-LSTM architecture which is designed to sequentially determine a prospective location to be searched next based on the history of the locations attended. We propose a reinforcement learning algorithm to train the model in an end-to-end manner, which allows to jointly learn the search paths and deep image features for matching. These properties together significantly reduce the number of windows to be evaluated and makes it robust to background clutters. Our model gives remarkable matching accuracy with the reduced number of windows and run time on MNIST and FlickrLogos-32 datasets.