Collaborative representation-based classification (CRC) is a famous representation-based classification method in pattern recognition. Recently, many variants of CRC have been designed for many classification tasks with the good classification performance. However, most of them ignore the inter-class pattern discrimination among the class-specific representations, which is very critical for strengthening the pattern discrimination of collaborative representation (CR). In this article, we propose a novel CR approach for image classification, called weighted discriminative collaborative competitive representation (WDCCR). The proposed WDCCR designs the discriminative and competitive collaborative representation among all the classes by fully considering the class information. On the one hand, we incorporate two discriminative constraints into the unified WDCCR model. Both constraints are the competitive class-specific representation residuals and the pairs of class-specific representations for each query sample. On the other hand, the constraint of the weighted categorical representation coefficients is introduced into the proposed model for further enhancing the power of discriminative and competitive representation. In the weighted constraint, we assume that the different classes of each query sample should have less contribution to the representation with the small representation coefficients, and then two types of weight factors are designed to constrain the representation coefficients. Furthermore, the robust WDCCR (R-WDCCR) is proposed with l1-norm representation fidelity for recognizing noisy images. Extensive experiments on six image data sets demonstrate the effective and robust superiorities of the proposed WDCCR and R-WDCCR over the related state-of-the-art representation-based classification methods.

中文翻译：

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加权判别式协作竞争表示，用于鲁棒的图像分类。

基于协作表示的分类（CRC）是模式识别中一种著名的基于表示的分类方法。近来，已经为具有良好分类性能的许多分类任务设计了CRC的许多变体。但是，它们中的大多数都忽略了特定于类的表示之间的类间模式歧视，这对于加强协作表示（CR）的模式歧视非常关键。在本文中，我们提出了一种用于图像分类的新颖CR方法，称为加权区分协作竞争表示（WDCCR）。拟议的WDCCR通过充分考虑班级信息来设计所有班级之间的区分性和竞争性协作代表。一方面，我们将两个区分性约束条件纳入统一的WDCCR模型。这两个约束都是竞争性的特定于类的表示残差以及每个查询样本的成对的特定于类的表示。另一方面，将加权分类表示系数的约束引入到所提出的模型中，以进一步增强区分和竞争表示的能力。在加权约束中，我们假设每个查询样本的不同类别对具有较小表示系数的表示的贡献应较小，然后设计两种类型的权重因子来约束表示系数。此外，提出了具有11范数表示保真度的鲁棒WDCCR（R-WDCCR），用于识别噪声图像。