Twin relaxed least squares regression with classwise mean constraint for image classification

被引:4
作者
Meenakshi [1 ]
Srirangarajan, Seshan [1 ,2 ]
机构
[1] Indian Inst Technol Delhi, Dept Elect Engn, New Delhi 110016, India
[2] Indian Inst Technol Delhi, Bharti Sch Telecommun Technol & Management, New Delhi 110016, India
关键词
Regression; Relaxed target; Image classification; Dimensionality reduction; ROBUST FACE RECOGNITION; K-SVD; DICTIONARY; ALGORITHM; ILLUMINATION; PROJECTION; MODELS; SCENE;
D O I
10.1016/j.imavis.2022.104506
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a twin relaxed least squares regression (TRLSR) framework with classwise mean constraint for image classification. The primary objective of TRLSR is to learn discriminative projections with enhanced interclass margins while preserving the intrinsic structure of the data. To this end, we introduce a relaxed regression target matrix together with a twin matrix to allow greater flexibility in learning the projections compared to using the conventional strict binary label matrix. In addition, a classwise mean constraint is introduced to retain the intraclass similarity of the data, which is beneficial in learning more discriminative projections. An l(2,1)-norm based regularization on the optimized projections is incorporated to extract more significant features while limiting the impact of noise and overfitting. The performance of the proposed technique on several public data sets for face recognition, object classification, action recognition and scene classification applications is demonstrated. The proposed method is shown to outperform the state-of-the-art approaches. (C) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:10
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