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. © Springer-Verlag 2010.
CITATION STYLE
Nagahashi, T., Fujiyoshi, H., & Kanade, T. (2010). Video segmentation using iterated graph cuts based on spatio-temporal volumes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5995 LNCS, pp. 655–666). https://doi.org/10.1007/978-3-642-12304-7_62
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