【摘要】 针对现有镜头聚类算法中选择最优化分类个数复杂度较高、分类结果准确性较低的问题,提出了一种基于谱分割理论的镜头聚类算法.通过对每个镜头集构造球状高斯模型SGM(Spherical Gaussian Model),最优化地拟合镜头数据集,提高镜头分割的准确性;在镜头迭代分类过程中采用谱分割算法以提高最终分类结果的准确性;在迭代分类过程中,采用贝叶斯信息准则BIC(Bayesian Information Criterion)作为分类停止的评判标准;最后根据BIC准则计算每两类融合前后的匹配值,判断比较后对分类结果进行融合,矫正在分类过程中同一类被割裂的错误.通过3类体育视频样本对算法的有效性进行了验证、比较和分析.更多还原

【Abstract】 A spectral-division unsupervised shot-clustering algorithm(SDUSC) was proposed.First,the keyframes were picked out to represent the shots,and the color features of keyframes were extracted to describe the video shots.In order to characterize shots optimally,a spherical gaussian model(SGM) was constructed for every shot category.Then the spectral division(SD) method was employed to divide a category into two categories,and the method was iteratively used to further divide the results of previous divisions.At the end of each iterative shot-division,bayesian information criterion(BIC) was utilized to automatically judge whether to stop further division.During the processes of shot-divisions,one category might be dissevered by mistake.In order to correct these mistakes,similar categories would be merged by calculating the similarities of all the possible category pairs.This approach was applied to three kinds of sports videos,and the experimental results shows that the proposed approach is reliable and effective.更多还原