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

We introduce a new concept of co-recognition for object-level image matching between an arbitrary image pair. Our method augments putative local regionmatches 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.

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Minsu Cho (Seoul National University), Young Min Shin (Seoul National University), and Kyoung Mu Lee (Seoul National University)
• Keywords: Co-Recognition, Computer Vision, Data-Driven Monte Carlo, ECCV 2008, Image Exploration