PT Unknown
AU Jose Carlos Rubio
   Joan Serrat
   Antonio Lopez
TI Unsupervised co-segmentation through region matching
BT 25th IEEE Conference on Computer Vision and Pattern Recognition
PY 2012
BP 749
EP 756
DI 10.1109/CVPR.2012.6247745
AB Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database.
ER