IEEE Trans Image Process - Learning conditional random fields for classification of hyperspectral images.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1057) registr(996) error(939) }
{ featur(3375) classif(2383) classifi(1994) }
{ method(1557) propos(1049) approach(1037) }
{ method(2212) result(1239) propos(1039) }
{ can(774) often(719) complex(702) }
{ framework(1458) process(801) describ(734) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ activ(1452) weight(1219) physic(1104) }
{ imag(2675) segment(2577) method(1081) }
{ problem(2511) optim(1539) algorithm(950) }
{ chang(1828) time(1643) increas(1301) }
{ model(3480) simul(1196) paramet(876) }
{ signal(2180) analysi(812) frequenc(800) }
{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
{ process(1125) use(805) approach(778) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ imag(2830) propos(1344) filter(1198) }
{ motion(1329) object(1292) video(1091) }
{ error(1145) method(1030) estim(1020) }
{ concept(1167) ontolog(924) domain(897) }
{ model(2220) cell(1177) simul(1124) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ studi(1119) effect(1106) posit(819) }
{ health(3367) inform(1360) care(1135) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ first(2504) two(1366) second(1323) }
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{ sequenc(1873) structur(1644) protein(1328) }
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{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
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{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
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{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
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{ import(1318) role(1303) understand(862) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
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{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ 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) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ 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) }
{ cancer(2502) breast(956) screen(824) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Hyperspectral images exhibit strong dependencies across spatial and spectral neighbors, which have been proved to be very useful for hyperspectral image classification. State-of-the-art hyperspectral image classification algorithms use the dependencies in a heuristic way or in probabilistic frameworks but impose unreasonable assumptions on observed data. In this paper, we formulate a conditional random field (CRF) to replace such heuristics and unreasonable assumptions for the classification of hyperspectral images. Moreover, because of avoiding explicit modeling of the observed data, the proposed method can incorporate the classification of hyperspectral images with different statistics characteristics into a unified probabilistic framework. Since the usual classification task for hyperspectral images needs the proposed CRF to be trained on local samples, available global training methods cannot be directly used. Under piecewise training framework, this paper develops an efficient local method to train the CRF. It is efficiently implemented through separated training of simple classifiers defined by corresponding potentials. However, the independent classifier training may lead to over-counting problems during inference. So we further propose a strategy to combine the independently trained models to obtain final CRF model. Experiments on real-world hyperspectral data show that our algorithm is competitive with the most recent results in hyperspectral image classification.

Resumo Limpo

hyperspectr imag exhibit strong depend across spatial spectral neighbor prove use hyperspectr imag classif stateoftheart hyperspectr imag classif algorithm use depend heurist way probabilist framework impos unreason assumpt observ data paper formul condit random field crf replac heurist unreason assumpt classif hyperspectr imag moreov avoid explicit model observ data propos method can incorpor classif hyperspectr imag differ statist characterist unifi probabilist framework sinc usual classif task hyperspectr imag need propos crf train local sampl avail global train method direct use piecewis train framework paper develop effici local method train crf effici implement separ train simpl classifi defin correspond potenti howev independ classifi train may lead overcount problem infer propos strategi combin independ train model obtain final crf model experi realworld hyperspectr data show algorithm competit recent result hyperspectr imag classif

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