IEEE Trans Image Process - Efficient image classification via multiple rank regression.

Tópicos

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{ model(2341) predict(2261) use(1141) }
{ problem(2511) optim(1539) algorithm(950) }
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{ method(1969) cluster(1462) data(1082) }
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{ can(774) often(719) complex(702) }
{ method(984) reconstruct(947) comput(926) }
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{ result(1111) use(1088) new(759) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
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{ signal(2180) analysi(812) frequenc(800) }
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{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
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Resumo

The problem of image classification has aroused considerable research interest in the field of image processing. Traditional methods often convert an image to a vector and then use a vector-based classifier. In this paper, a novel multiple rank regression model (MRR) for matrix data classification is proposed. Unlike traditional vector-based methods, we employ multiple-rank left projecting vectors and right projecting vectors to regress each matrix data set to its label for each category. The convergence behavior, initialization, computational complexity, and parameter determination are also analyzed. Compared with vector-based regression methods, MRR achieves higher accuracy and has lower computational complexity. Compared with traditional supervised tensor-based methods, MRR performs better for matrix data classification. Promising experimental results on face, object, and hand-written digit image classification tasks are provided to show the effectiveness of our method.

Resumo Limpo

problem imag classif arous consider research interest field imag process tradit method often convert imag vector use vectorbas classifi paper novel multipl rank regress model mrr matrix data classif propos unlik tradit vectorbas method employ multiplerank left project vector right project vector regress matrix data set label categori converg behavior initi comput complex paramet determin also analyz compar vectorbas regress method mrr achiev higher accuraci lower comput complex compar tradit supervis tensorbas method mrr perform better matrix data classif promis experiment result face object handwritten digit imag classif task provid show effect method

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