Abstract: Estimating dense correspondences between images is a long-standing image understanding task. Most recent works introduce convolutional neural networks to extract high-level feature maps and find correspondences through feature matching. However, high-level feature maps are in low spatial resolution and therefore insufficient to provide accurate and fine-grained features to distinguish intra-class variations for correspondence matching. To address this problem, we generate robust features by selecting and combining convolutional features at different levels/scales. To resolve two critical issues in feature selection, i.e., how many and which levels of features to be selected, we frame the feature selection process as a sequential Markov decision-making process (MDP)and introduce an optimal selection strategy using reinforcement learning (RL) to select features. Particularly, we define an RL environment for image matching in which individual actions are either requests for new features or terminate the selection episode by referring a matching score. Deep neural networks are incorporated into our method and trained for decision making. Experimental results show that our method achieves com-parable/superior performance with state-of-the-art methods on three public benchmarks, demonstrating the effectiveness of our proposed feature selection strategy.