We are excited to announce that our paper “Deep reinforcement image matching with self-termination” has been accepted to ICIP2021.
This paper deals with the problem of deep reinforcement learning-based image matching, which enables us to sequentially searches only the most promising regions in the reference image that match the query. Since existing methods do not have any function to judge whether the target region has been successfully identified or not, they continue to search until the preset maximum number of search steps is reached. In this paper, we propose a deep image matching network that can terminate the matching process by itself. Our network is designed to have a halting module that identifies whether the current reference region matches the query based on the image features and the search history. The entire network is effectively trained end-to-end in a framework of deep reinforcement learning that incorporates a new loss function to evaluate the accuracy of the termination decision. Experimental results demonstrate that our method can achieve highly competitive or better matching accuracy with fewer search steps than the existing methods.
The paper can be accessible on IEEE Xplore:
