IEEE Trans Pattern Anal Mach Intell - Tag Completion for Image Retrieval.

Tópicos

{ problem(2511) optim(1539) algorithm(950) }
{ general(901) number(790) one(736) }
{ studi(1119) effect(1106) posit(819) }
{ search(2224) databas(1162) retriev(909) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ assess(1506) score(1403) qualiti(1306) }
{ extract(1171) text(1153) clinic(932) }
{ howev(809) still(633) remain(590) }
{ can(981) present(881) function(850) }
{ motion(1329) object(1292) video(1091) }
{ chang(1828) time(1643) increas(1301) }
{ visual(1396) interact(850) tool(830) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ sequenc(1873) structur(1644) protein(1328) }
{ patient(2315) diseas(1263) diabet(1191) }
{ data(1714) softwar(1251) tool(1186) }
{ model(2220) cell(1177) simul(1124) }
{ data(3963) clinic(1234) research(1004) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ use(976) code(926) identifi(902) }
{ process(1125) use(805) approach(778) }
{ model(3404) distribut(989) bayesian(671) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ 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

Many social image search engines are based on keyword/tag matching. This is because tag-based image retrieval (TBIR) is not only efficient but also effective. The performance of TBIR is highly dependent on the availability and quality of manual tags. Recent studies have shown that manual tags are often unreliable and inconsistent. In addition, since many users tend to choose general and ambiguous tags in order to minimize their efforts in choosing appropriate words, tags that are specific to the visual content of images tend to be missing or noisy, leading to a limited performance of TBIR. To address this challenge, we study the problem of tag completion, where the goal is to automatically fill in the missing tags as well as correct noisy tags for given images. We represent the image-tag relation by a tag matrix, and search for the optimal tag matrix consistent with both the observed tags and the visual similarity. We propose a new algorithm for solving this optimization problem. Extensive empirical studies show that the proposed algorithm is significantly more effective than the state-of-the-art algorithms. Our studies also verify that the proposed algorithm is computationally efficient and scales well to large databases.

Resumo Limpo

mani social imag search engin base keywordtag match tagbas imag retriev tbir effici also effect perform tbir high depend avail qualiti manual tag recent studi shown manual tag often unreli inconsist addit sinc mani user tend choos general ambigu tag order minim effort choos appropri word tag specif visual content imag tend miss noisi lead limit perform tbir address challeng studi problem tag complet goal automat fill miss tag well correct noisi tag given imag repres imagetag relat tag matrix search optim tag matrix consist observ tag visual similar propos new algorithm solv optim problem extens empir studi show propos algorithm signific effect stateoftheart algorithm studi also verifi propos algorithm comput effici scale well larg databas

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