TY - JOUR T1 - Unsupervised learning applied to progressive compression of time-dependent geometry JF - Computers & Graphics Y1 - 2005 A1 - Baby,Thomas A1 - Kim,Youngmin A1 - Varshney, Amitabh KW - Clustering algorithms KW - Distributed/network graphics KW - pattern recognition AB - We propose a new approach to progressively compress time-dependent geometry. Our approach exploits correlations in motion vectors to achieve better compression. We use unsupervised learning techniques to detect good clusters of motion vectors. For each detected cluster, we build a hierarchy of motion vectors using pairwise agglomerative clustering, and succinctly encode the hierarchy using entropy encoding. We demonstrate our approach on a client–server system that we have built for downloading time-dependent geometry. VL - 29 SN - 0097-8493 UR - http://www.sciencedirect.com/science/article/pii/S009784930500052X CP - 3 M3 - 10.1016/j.cag.2005.03.021 ER -