IEEE Trans Image Process - Self-supervised online metric learning with low rank constraint for scene categorization.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ perform(999) metric(946) measur(919) }
{ take(945) account(800) differ(722) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ model(2656) set(1616) predict(1553) }
{ survey(1388) particip(1329) question(1065) }
{ featur(1941) imag(1645) propos(1176) }
{ visual(1396) interact(850) tool(830) }
{ estim(2440) model(1874) function(577) }
{ problem(2511) optim(1539) algorithm(950) }
{ system(1976) rule(880) can(841) }
{ imag(2675) segment(2577) method(1081) }
{ motion(1329) object(1292) video(1091) }
{ result(1111) use(1088) new(759) }
{ process(1125) use(805) approach(778) }
{ sampl(1606) size(1419) use(1276) }
{ structur(1116) can(940) graph(676) }
{ measur(2081) correl(1212) valu(896) }
{ treatment(1704) effect(941) patient(846) }
{ error(1145) method(1030) estim(1020) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ care(1570) inform(1187) nurs(1089) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ studi(1119) effect(1106) posit(819) }
{ model(3480) simul(1196) paramet(876) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ activ(1138) subject(705) human(624) }
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{ general(901) number(790) one(736) }
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{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
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{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Conventional visual recognition systems usually train an image classifier in a bath mode with all training data provided in advance. However, in many practical applications, only a small amount of training samples are available in the beginning and many more would come sequentially during online recognition. Because the image data characteristics could change over time, it is important for the classifier to adapt to the new data incrementally. In this paper, we present an online metric learning method to address the online scene recognition problem via adaptive similarity measurement. Given a number of labeled data followed by a sequential input of unseen testing samples, the similarity metric is learned to maximize the margin of the distance among different classes of samples. By considering the low rank constraint, our online metric learning model not only can provide competitive performance compared with the state-of-the-art methods, but also guarantees convergence. A bi-linear graph is also defined to model the pair-wise similarity, and an unseen sample is labeled depending on the graph-based label propagation, while the model can also self-update using the more confident new samples. With the ability of online learning, our methodology can well handle the large-scale streaming video data with the ability of incremental self-updating. We evaluate our model to online scene categorization and experiments on various benchmark datasets and comparisons with state-of-the-art methods demonstrate the effectiveness and efficiency of our algorithm.

Resumo Limpo

convent visual recognit system usual train imag classifi bath mode train data provid advanc howev mani practic applic small amount train sampl avail begin mani come sequenti onlin recognit imag data characterist chang time import classifi adapt new data increment paper present onlin metric learn method address onlin scene recognit problem via adapt similar measur given number label data follow sequenti input unseen test sampl similar metric learn maxim margin distanc among differ class sampl consid low rank constraint onlin metric learn model can provid competit perform compar stateoftheart method also guarante converg bilinear graph also defin model pairwis similar unseen sampl label depend graphbas label propag model can also selfupd use confid new sampl abil onlin learn methodolog can well handl largescal stream video data abil increment selfupd evalu model onlin scene categor experi various benchmark dataset comparison stateoftheart method demonstr effect effici algorithm

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