2008-10-01

[IC] Graph-based High Level Motion Segmentation using Normalized Cuts

Abstract.

Motion capture devices have been utilized inproducing several contents, such as movies and video games. However,since motion capture devices are expensive and inconvenient to use,motions segmented from captured data was recycled and synthesizedto utilize it in another contents, but the motions were generallysegmented by contents producers in manual. Therefore, automaticmotion segmentation is recently getting a lot of attentions. Previousapproaches are divided into on-line and off-line, where on-lineapproaches segment motions based on similarities betweenneighboring frames and off-line approaches segment motions bycapturing the global characteristics in feature space. In this paper, wepropose a graph-based high-level motion segmentation method. Sincehigh-level motions consist of several repeated frames within temporaldistances, we consider all similarities among all frames within thetemporal distance. This is achieved by constructing a graph, whereeach vertex represents a frame and the edges between the frames areweighted by their similarity. Then, normalized cuts algorithm is usedto partition the constructed graph into several sub-graphs by globallyfinding minimum cuts. In the experiments, the results using theproposed method showed better performance than PCA-based methodin on-line and GMM-based method in off-line, as the proposed methodglobally segment motions from the graph constructed basedsimilarities between neighboring frames as well as similarities amongall frames within temporal distances.
Keywords: Capture Devices, High-Level Motions, Motion Segmentation, Normalized Cuts.

Click
http://hci.ssu.ac.kr/ajpark/[IC]MotionSegmentation.pdf
to download the paper.

2008.

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