Graph Cuts Based Image Segmentation with Part-Based Models

This study proposed an improved pre-labeling method based on deformable part models and HOG features for interactive segmentation with graph cuts. Because of the complex appearance of foreground and background, the result of segmentation is unsatisfactory. Many priors have been introduced into graph cuts to improve the segmentation results and our work is inspired by the shape prior. In this paper we use the deformable part-based model and HOG features to pre-label the seeds before the graph cuts algorithm. The user involvement is reduced and the performance of the graph cuts algorithm is improved at the first iteration. Our assumption is based on the compact shape. We assume that the area between the center of the part filter and root filter belongs to foreground. If the area covered by more filters, it will more probably be the foreground. Our results show that our method can get more accurate result especially the appearance of the object and background is similar and the shape of the object close to rectangle and eclipse.