A Semi-supervised Clustering-based Segmentation Algorithm of 3D Reconstructed Human Body Parts
基于半监督聚类的3维肢体分割算法

Human activity analysis is receiving increasing attention from computer vision researchers.One challenge is the segmentation of human body into meaningful body parts.A semi-supervised clustering-based body parts segmentation algorithm of 3D reconstructed human is presented in this paper.Firstly,we segment human body parts with the help of posture parameters of the previous frame.Then the structure information of human body is adopted to classify some points and initialize the centers of the semi-supervised clustering.Finally,based on the shape of body parts,semi-supervised clustering method is used to segment the body parts.In addition,body posture parameters are estimated with the segmentation result of body parts.The system is validated with IXMAS database,which includes 6 actors and 6 kinds of activity.The experimental results show that the presented algorithm can adapt the variety of views,actors and activitiesy.