Discriminant Projection Representation-based Classification for Vision Recognition

TL;DR

This paper introduces Discriminant Projection Representation-based Classification (DPRC), a novel method that iteratively approximates ideal sample representations for improved vision recognition, outperforming existing methods on multiple datasets.

Contribution

The paper proposes a new discriminant projection representation method (DPRC) that enhances classification accuracy by maximizing between-class and minimizing within-class reconstruction errors.

Findings

DPRC outperforms state-of-the-art methods on five vision recognition datasets.

PRC effectively approximates ideal sample representations through iterative projections.

Experimental results demonstrate the robustness and effectiveness of DPRC in various recognition tasks.

Abstract

Representation-based classification methods such as sparse representation-based classification (SRC) and linear regression classification (LRC) have attracted a lot of attentions. In order to obtain the better representation, a novel method called projection representation-based classification (PRC) is proposed for image recognition in this paper. PRC is based on a new mathematical model. This model denotes that the 'ideal projection' of a sample point $x$ on the hyper-space $H$ may be gained by iteratively computing the projection of $x$ on a line of hyper-space $H$ with the proper strategy. Therefore, PRC is able to iteratively approximate the 'ideal representation' of each subject for classification. Moreover, the discriminant PRC (DPRC) is further proposed, which obtains the discriminant information by maximizing the ratio of the between-class reconstruction error over the within-class reconstruction error. Experimental results on five typical databases show that the proposed PRC and DPRC are effective and outperform other state-of-the-art methods on several vision recognition tasks.