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

Abstract

• A novel twin relaxed regression model is introduced for image classification. • A relaxed target matrix together with a twin matrix provide more degrees of freedom to fit the class labels. • Enlarged interclass margins for improved classification. • Adaptively maximize the intraclass similarity with a classwise mean constraint. • Experimental results show that the proposed method outperforms state-of-the-art algorithms in terms of classification rate. 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.