Collaborative representation is an effective way to design classifiers for many practical applications. In this paper, we propose a novel classifier, called the prior knowledge-based probabilistic collaborative representation-based classifier (PKPCRC), for visual recognition. Compared with existing classifiers which use the collaborative representation strategy, the proposed PKPCRC further includes characteristics of training samples of each class as prior knowledge. Four types of prior knowledge are developed from the perspectives of image distance and representation capacity. They adaptively accommodate the contribution of each class and result in an accurate representation to classify a query sample. Experiments and comparisons on four challenging databases demonstrate that PKPCRC outperforms several state-of-the-art classifiers.

中文翻译：

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用于视觉识别的基于先验知识的概率协作表示


协作表示是为许多实际应用设计分类器的有效方法。在本文中，我们提出了一种用于视觉识别的新型分类器，称为基于先验知识的概率协作表示分类器（PKPCRC）。与使用协作表示策略的现有分类器相比，所提出的 PKPCRC 进一步包括每个类别的训练样本的特征作为先验知识。从图像距离和表示能力的角度发展了四种类型的先验知识。它们自适应地适应每个类别的贡献，并产生准确的表示来对查询样本进行分类。对四个具有挑战性的数据库的实验和比较表明 PKPCRC 优于几种最先进的分类器。