IEEE Trans Pattern Anal Mach Intell - Image Signature: Highlighting Sparse Salient Regions.

Tópicos

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{ spatial(1525) area(1432) region(1030) }
{ learn(2355) train(1041) set(1003) }
{ method(2212) result(1239) propos(1039) }
{ featur(1941) imag(1645) propos(1176) }
{ framework(1458) process(801) describ(734) }
{ chang(1828) time(1643) increas(1301) }
{ method(984) reconstruct(947) comput(926) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ import(1318) role(1303) understand(862) }
{ use(976) code(926) identifi(902) }
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{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
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{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
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{ method(1557) propos(1049) approach(1037) }
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{ studi(1119) effect(1106) posit(819) }
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{ take(945) account(800) differ(722) }
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{ care(1570) inform(1187) nurs(1089) }
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{ patient(2837) hospit(1953) medic(668) }
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{ 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) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
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{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We introduce a simple image descriptor referred to as the image signature. We show, within the theoretical framework of sparse signal mixing, that this quantity spatially approximates the foreground of an image. We experimentally investigate whether this approximate foreground overlaps with visually conspicuous image locations by developing a saliency algorithm based on the image signature. This saliency algorithm predicts human fixation points best among competitors on the Bruce and Tsotsos [1] benchmark data set and does so in much shorter running time. In a related experiment, we demonstrate with a change blindness data set that the distance between images induced by the image signature is closer to human perceptual distance than can be achieved using other saliency algorithms, pixel-wise, or GIST [2] descriptor methods.

Resumo Limpo

introduc simpl imag descriptor refer imag signatur show within theoret framework spars signal mix quantiti spatial approxim foreground imag experiment investig whether approxim foreground overlap visual conspicu imag locat develop salienc algorithm base imag signatur salienc algorithm predict human fixat point best among competitor bruce tsotso benchmark data set much shorter run time relat experi demonstr chang blind data set distanc imag induc imag signatur closer human perceptu distanc can achiev use salienc algorithm pixelwis gist descriptor method

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