IEEE Trans Neural Netw Learn Syst - Ordinal Distance Metric Learning for Image Ranking.

Tópicos

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{ imag(1947) propos(1133) code(1026) }
{ model(3480) simul(1196) paramet(876) }
{ algorithm(1844) comput(1787) effici(935) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ perform(999) metric(946) measur(919) }
{ take(945) account(800) differ(722) }
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{ data(3963) clinic(1234) research(1004) }
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{ data(1737) use(1416) pattern(1282) }
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{ sampl(1606) size(1419) use(1276) }
{ can(981) present(881) function(850) }
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{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Recently, distance metric learning (DML) has attracted much attention in image retrieval, but most previous methods only work for image classification and clustering tasks. In this brief, we focus on designing ordinal DML algorithms for image ranking tasks, by which the rank levels among the images can be well measured. We first present a linear ordinal Mahalanobis DML model that tries to preserve both the local geometry information and the ordinal relationship of the data. Then, we develop a nonlinear DML method by kernelizing the above model, considering of real-world image data with nonlinear structures. To further improve the ranking performance, we finally derive a multiple kernel DML approach inspired by the idea of multiple-kernel learning that performs different kernel operators on different kinds of image features. Extensive experiments on four benchmarks demonstrate the power of the proposed algorithms against some related state-of-the-art methods.

Resumo Limpo

recent distanc metric learn dml attract much attent imag retriev previous method work imag classif cluster task brief focus design ordin dml algorithm imag rank task rank level among imag can well measur first present linear ordin mahalanobi dml model tri preserv local geometri inform ordin relationship data develop nonlinear dml method kernel model consid realworld imag data nonlinear structur improv rank perform final deriv multipl kernel dml approach inspir idea multiplekernel learn perform differ kernel oper differ kind imag featur extens experi four benchmark demonstr power propos algorithm relat stateoftheart method

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