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.