Co-recognition of Image Pairs by Data-Driven Monte Carlo Image Exploration

Minsu Cho, Young Min Shin, Kyoung Mu Lee

Abstract: We introduce a new concept of ‘co-recognition’ for object-level image matching between an arbitrary image pair. Our method augments putative local region matches to reliable object-level correspondences without any supervision or prior knowledge on common objects. It provides the number of reliable common objects and the dense correspondences between the image pair. In this paper, generative model for co-recognition is presented. For inference, we propose data-driven Monte Carlo image exploration which clusters and propagates local region matches by Markov chain dynamics. The global optimum is achieved by a guiding force of our data-driven sampling and posterior probability model. In the experiments, we demonstrate the power and utility on image retrieval and unsupervised recognition and segmentation of multiple common objects.