Class-Specific Segmentation Using Layered Pictorial Structures
Prof. Philip H. S. Torr (Oxford Brookes University, UK)
We propose a novel method for recognizing and segmenting instances of an
object category from images using the layered pictorial structures
model. Included in this model are the effects of self-occlusion which
makes it particularly suitable for such applications.
An unsupervised method for learning the model from videos is presented.
Given an image, we match the model to localize the instance(s) of the
object. The matching is made efficient by substantially improving the
running time of match scores computation and belief propagation. This
localization allows us to learn the distribution of RGB values for
`figure' and `ground'. Using the learnt distributions, graph cuts are
employed to efficiently perform segmentation of objects from the image.