  Most existing matching algorithms are one-off algorithms, i.e., they usually
measure the distance between the two image feature representation vectors for
only one time. In contrast, human's vision system achieves this task, i.e.,
image matching, by recursively looking at specific/related parts of both images
and then making the final judgement. Towards this end, we propose a novel loopy
recurrent neural network (Loopy RNN), which is capable of aggregating
relationship information of two input images in a progressive/iterative manner
and outputting the consolidated matching score in the final iteration. A Loopy
RNN features two uniqueness. First, built on conventional long short-term
memory (LSTM) nodes, it links the output gate of the tail node to the input
gate of the head node, thus it brings up symmetry property required for
matching. Second, a monotonous loss designed for the proposed network
guarantees increasing confidence during the recursive matching process.
Extensive experiments on several image matching benchmarks demonstrate the
great potential of the proposed method.
