IEEE Trans Pattern Anal Mach Intell - Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

Tópicos

{ motion(1329) object(1292) video(1091) }
{ imag(2675) segment(2577) method(1081) }
{ method(1969) cluster(1462) data(1082) }
{ learn(2355) train(1041) set(1003) }
{ data(1714) softwar(1251) tool(1186) }
{ gene(2352) biolog(1181) express(1162) }
{ control(1307) perform(991) simul(935) }
{ general(901) number(790) one(736) }
{ visual(1396) interact(850) tool(830) }
{ imag(1947) propos(1133) code(1026) }
{ bind(1733) structur(1185) ligand(1036) }
{ chang(1828) time(1643) increas(1301) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ perform(1367) use(1326) method(1137) }
{ sequenc(1873) structur(1644) protein(1328) }
{ extract(1171) text(1153) clinic(932) }
{ risk(3053) factor(974) diseas(938) }
{ studi(1119) effect(1106) posit(819) }
{ data(1737) use(1416) pattern(1282) }
{ measur(2081) correl(1212) valu(896) }
{ method(1219) similar(1157) match(930) }
{ assess(1506) score(1403) qualiti(1306) }
{ research(1085) discuss(1038) issu(1018) }
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{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
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{ patient(2837) hospit(1953) medic(668) }
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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
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{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
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{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
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{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

Resumo Limpo

tempor segment human motion plausibl motion primit central understand build comput model human motion sever issu contribut challeng discov motion primit exponenti natur possibl movement combin variabl tempor scale human action complex repres articul motion pose problem learn motion primit one tempor cluster deriv unsupervis hierarch bottomup framework call hierarch align cluster analysi haca haca find partit given multidimension time seri m disjoint segment segment belong one k cluster haca combin kernel kmean general dynam time align kernel cluster time seri data moreov provid natur framework find lowdimension embed time seri haca effici optim coordin descent strategi dynam program experiment result motion captur video data demonstr effect haca segment complex motion visual tool also compar perform haca stateoftheart algorithm tempor cluster data honey bee danc haca code avail onlin

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