POSIT: Part-based object segmentation without intensive training

Object segmentation is a well-known difficult problem in pattern recognition. Until now, most of the existing object segmentation methods need to go through a time-consuming training phase prior to segmentation. Both robustness and efficiency of the existing methods have room for improvement. In this work, we propose a new methodology, called POSIT, for object segmentation without intensive training process. We construct a part-based shape model to substitute the training process. In the part-based framework, we sequentially register object parts in the prior model to an image so that the searching space is largely reduced. Another advantage of the sequential matching is that, instead of predefining the weighting parameters for the terms in the matching evaluation function, we can estimate the parameters in our model on the fly. Finally, we fine-tune the previous coarse segmentation by localized graph cuts. In the experiments, POSIT has been tested on numerous natural horse and cow images and the obtained results show the accuracy, robustness and efficiency of the proposed object segmentation method.