The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, apresentamos um novo método de aprendizagem discriminativa de dicionário (DDL) para classificação de imagens. O relacionamento estrutural local entre as amostras é primeiro construído pelos automapas Laplacianos (LE) e depois integrado ao quadro DDL básico para suprimir a ambigüidade entre classes no espaço de recursos. Além disso, a fim de melhorar a capacidade discriminativa do dicionário, as informações do rótulo da categoria das amostras de treinamento são formuladas na função objetivo da aprendizagem do dicionário, considerando o termo de promoção discriminativo. Assim, os pontos de dados das amostras originais são transformados em um novo espaço de características, no qual se espera que os pontos de diferentes categorias estejam distantes uns dos outros. Os resultados dos testes baseados no conjunto de dados reais indicam a eficácia deste método.
Wentao LYU
Zhejiang Sci-Tech University
Di ZHOU
Zhejiang Uniview Technologies Co., Ltd.
Chengqun WANG
Zhejiang Sci-Tech University
Lu ZHANG
Zhejiang Sci-Tech University
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Wentao LYU, Di ZHOU, Chengqun WANG, Lu ZHANG, "A Novel Discriminative Dictionary Learning Method for Image Classification" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 6, pp. 932-937, June 2023, doi: 10.1587/transfun.2022EAP1149.
Abstract: In this paper, we present a novel discriminative dictionary learning (DDL) method for image classification. The local structural relationship between samples is first built by the Laplacian eigenmaps (LE), and then integrated into the basic DDL frame to suppress inter-class ambiguity in the feature space. Moreover, in order to improve the discriminative ability of the dictionary, the category label information of training samples is formulated into the objective function of dictionary learning by considering the discriminative promotion term. Thus, the data points of original samples are transformed into a new feature space, in which the points from different categories are expected to be far apart. The test results based on the real dataset indicate the effectiveness of this method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAP1149/_p
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@ARTICLE{e106-a_6_932,
author={Wentao LYU, Di ZHOU, Chengqun WANG, Lu ZHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Novel Discriminative Dictionary Learning Method for Image Classification},
year={2023},
volume={E106-A},
number={6},
pages={932-937},
abstract={In this paper, we present a novel discriminative dictionary learning (DDL) method for image classification. The local structural relationship between samples is first built by the Laplacian eigenmaps (LE), and then integrated into the basic DDL frame to suppress inter-class ambiguity in the feature space. Moreover, in order to improve the discriminative ability of the dictionary, the category label information of training samples is formulated into the objective function of dictionary learning by considering the discriminative promotion term. Thus, the data points of original samples are transformed into a new feature space, in which the points from different categories are expected to be far apart. The test results based on the real dataset indicate the effectiveness of this method.},
keywords={},
doi={10.1587/transfun.2022EAP1149},
ISSN={1745-1337},
month={June},}
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TY - JOUR
TI - A Novel Discriminative Dictionary Learning Method for Image Classification
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 932
EP - 937
AU - Wentao LYU
AU - Di ZHOU
AU - Chengqun WANG
AU - Lu ZHANG
PY - 2023
DO - 10.1587/transfun.2022EAP1149
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2023
AB - In this paper, we present a novel discriminative dictionary learning (DDL) method for image classification. The local structural relationship between samples is first built by the Laplacian eigenmaps (LE), and then integrated into the basic DDL frame to suppress inter-class ambiguity in the feature space. Moreover, in order to improve the discriminative ability of the dictionary, the category label information of training samples is formulated into the objective function of dictionary learning by considering the discriminative promotion term. Thus, the data points of original samples are transformed into a new feature space, in which the points from different categories are expected to be far apart. The test results based on the real dataset indicate the effectiveness of this method.
ER -