IEEE Trans Image Process - Artistic image analysis using graph-based learning approaches.

Tópicos

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{ problem(2511) optim(1539) algorithm(950) }
{ perform(999) metric(946) measur(919) }
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{ method(2212) result(1239) propos(1039) }
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{ analysi(2126) use(1163) compon(1037) }
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{ imag(2675) segment(2577) method(1081) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
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{ structur(1116) can(940) graph(676) }
{ can(774) often(719) complex(702) }
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{ ehr(2073) health(1662) electron(1139) }
{ 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) }
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{ intervent(3218) particip(2042) group(1664) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We introduce a new methodology for the problem of artistic image analysis, which among other tasks, involves the automatic identification of visual classes present in an art work. In this paper, we advocate the idea that artistic image analysis must explore a graph that captures the network of artistic influences by computing the similarities in terms of appearance and manual annotation. One of the novelties of our methodology is the proposed formulation that is a principled way of combining these two similarities in a single graph. Using this graph, we show that an efficient random walk algorithm based on an inverted label propagation formulation produces more accurate annotation and retrieval results compared with the following baseline algorithms: bag of visual words, label propagation, matrix completion, and structural learning. We also show that the proposed approach leads to a more efficient inference and training procedures. This experiment is run on a database containing 988 artistic images (with 49 visual classification problems divided into a multiclass problem with 27 classes and 48 binary problems), where we show the inference and training running times, and quantitative comparisons with respect to several retrieval and annotation performance measures.

Resumo Limpo

introduc new methodolog problem artist imag analysi among task involv automat identif visual class present art work paper advoc idea artist imag analysi must explor graph captur network artist influenc comput similar term appear manual annot one novelti methodolog propos formul principl way combin two similar singl graph use graph show effici random walk algorithm base invert label propag formul produc accur annot retriev result compar follow baselin algorithm bag visual word label propag matrix complet structur learn also show propos approach lead effici infer train procedur experi run databas contain artist imag visual classif problem divid multiclass problem class binari problem show infer train run time quantit comparison respect sever retriev annot perform measur

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