IEEE Trans Image Process - K-NN regression to improve statistical feature extraction for texture retrieval.

Tópicos

{ featur(1941) imag(1645) propos(1176) }
{ featur(3375) classif(2383) classifi(1994) }
{ search(2224) databas(1162) retriev(909) }
{ perform(1367) use(1326) method(1137) }
{ estim(2440) model(1874) function(577) }
{ take(945) account(800) differ(722) }
{ clinic(1479) use(1117) guidelin(835) }
{ can(981) present(881) function(850) }
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{ research(1085) discuss(1038) issu(1018) }
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{ high(1669) rate(1365) level(1280) }
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{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
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{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
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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) }
{ process(1125) use(805) approach(778) }
{ 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

This correspondence presents an iterative method based upon k -nearest neighbors ( k-NN) regression to improve the performance of statistical feature extraction for texture image retrieval. The idea exploits the fact that an ideal feature extraction system would extract similar signatures from images characterized by the same texture and different signatures from dissimilar textures. Under the assumption that conventional statistical feature extraction contributes to sufficiently good retrieval performance, the signatures of k retrieved textures are used to update the signature of the query image using the k -NN regression algorithm. Extensive experiments show significant improvements with respect to retrieval performance in comparison to conventional statistical feature extraction.

Resumo Limpo

correspond present iter method base upon k nearest neighbor knn regress improv perform statist featur extract textur imag retriev idea exploit fact ideal featur extract system extract similar signatur imag character textur differ signatur dissimilar textur assumpt convent statist featur extract contribut suffici good retriev perform signatur k retriev textur use updat signatur queri imag use k nn regress algorithm extens experi show signific improv respect retriev perform comparison convent statist featur extract

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