【摘要】 视频图像分割是图像处理领域的一个热门问题。在传统分割算法的基础上,提出了一种新的无监督的视频分割算法。该算法采用超像素对运动前景进行表示,定义联接权概念来描述超像素属于同一物体的可能性,并利用当前帧的静态特征与前后帧的关联特征进行联接权计算。为优化超像素间匹配关系的搜索,算法引入了超像素颜色特征匹配约束与运动关联性匹配约束的机制。分别在简单场景和复杂场景进行了视频分割实验,简单场景下,算法保证了较高的召回率与稳定的准确率;复杂场景下,算法完成了人群中单个人的切分。大量实验结果表明,该算法能够实现视频图像的分割,并且能有效解决过分割问题。更多还原

【Abstract】 Video segmentation is a hot issue in the field of image processing.Based on traditional segmentation algorithms,a new unsupervised video segmentation algorithm was proposed.This algorithm represents the moving foreground with superpixel algorithm,defines the join weight of superpixels as the possibility from the same object,and calculates the join weight with static features from current frame associated with the relevance feature between frames.In order to optimize the search of relevance match between superpixels from different frames,the algorithm introduces superpixel color feature constraint and movement constraint.The experiment contains two aspects,and the algorithm ensures higher recall rate and stable precision rate in the simple scenario and completes single person segmentation from the crowd in the complex scenes.Large numbers of experiments show that the proposed algorithm can realize video image segmentation,and effectively solve the problem of over-segmentation.更多还原