Temporal Coherent Video Segmentation with Support Vector Machine and Graph Cut

In order to improve the stability of video segmentation tasks,an algorithm is proposed which combines the graph cut algorithm with support vector machine.Firstly,the algorithm keeps the background static in adjacent frames with a preprocessing step.Then,the first frame is segmented with the mean shift algorithm.After that,a support vector machine is trained with sampled pixel features（i.e.positions and color information） from the segmentation of previous frames.It then outputs corresponding probability of pixels,which is regarded as energy of graph cut algorithms to output stable segmentations.Experiments show that the proposed algorithm performs better than traditional graph cut video segmentation algorithms from the perspective of temporal coherence.Meanwhile,the proposed algorithm could be combined with other existing video segmentation algorithms by importing the probability outputted by support vector machines.