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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 이미지 분류를 위한 새로운 차별적 사전 학습(DDL) 방법을 제시합니다. 샘플 간의 로컬 구조적 관계는 먼저 라플라시안 고유맵(LE)에 의해 구축된 다음 기본 DDL 프레임에 통합되어 특징 공간에서 클래스 간 모호성을 억제합니다. 또한, 사전의 변별력을 향상시키기 위해 훈련 샘플의 카테고리 라벨 정보를 변별적 촉진항을 고려하여 사전 학습의 목적 함수로 공식화한다. 따라서 원본 샘플의 데이터 포인트는 서로 다른 범주의 포인트가 멀리 떨어져 있을 것으로 예상되는 새로운 특징 공간으로 변환됩니다. 실제 데이터 세트를 기반으로 한 테스트 결과는 이 방법의 효율성을 나타냅니다.
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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부
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
부
@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},}
부
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 -