Video Segmentation Using Iterated Graph Cuts Based on Spatio-temporal Volumes

We present a novel approach to segmenting video using iterated graph cuts based on spatio-temporal volumes. We use the mean shift clustering algorithm to build the spatio-temporal volumes with different bandwidths from the input video. We compute the prior probability obtained by the likelihood from a color histogram and a distance transform using the segmentation results from graph cuts in the previous process, and set the probability as the t-link of the graph for the next process. The proposed method can segment regions of an object with a stepwise process from global to local segmentation by iterating the graph-cuts process with mean shift clustering using a different bandwidth. It is possible to reduce the number of nodes and edges to about 1/25 compared to the conventional method with the same segmentation rate.