Segmentation using Deformable Spatial Priors with Application to Clothing

We present a method for segmenting the parts of multiple instances of a known object category exhibiting large variations in projected shape and colour. The method builds on an existing MRF formulation incorporating a prior shape model and colour distributions for the constituent parts. We propose a novel shape model consisting of a deformable spatial prior probability for the part-label at each pixel. We also make a simple extension to the MRF formulation to deal simultaneously with multiple objects within a global optimisation. Finally, we evaluate the method for the task of segmenting individual items of clothing in images depicting groups of people, and demonstrate improved performance against the state of the art for this task.