Unsupervised Detection and Segmentation of Identical Objects

We address an unsupervised object detection and segmentation problem that goes beyond the conventional assumptions of one-to-one object correspondences or model-test settings between images. Our method can detect and segment identical objects directly from a single image or a handful of images without any supervision. To detect and segment all the object-level correspondences from the given images, a novel multi-layer match-growing method is proposed that starts from initial local feature matches and explores the images by intra-layer expansion and inter-layer merge. It estimates geometric relations between object entities and establishes ‘object correspondence networks’ that connect matching objects. Experiments demonstrate robust performance of our method on challenging datasets.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Minsu Cho (Seoul National University), Young Min Shin (Seoul National University), Kyoung Mu Lee (Seoul National University)
• Keywords: 2010, Computer Vision, Cvpr, CVPR 2010, Unsupervised Segmentation