The concept of picture fuzzy sets (PFS) is a generalization of ordinary fuzzy sets and intuitionistic fuzzy sets, which is characterized by positive membership, neutral membership, and negative membership functions. Keeping in mind the importance of similarity measures and applications in data mining, medical diagnosis, decision making, and pattern recognition, several studies have been proposed in the literature. Some of those, however, cannot satisfy the axioms of similarity and provide counter-intuitive cases. In this paper, we propose new similarity measures for PFSs based on two parameters t and p, where t identifies the level of uncertainty and p is the $L_p$ norm. The properties of the bi-parametric similarity and distance measures are discussed. We provide some counterexamples for existing similarity measures in the literature and show how our proposed similarity measure is important and applicable to the pattern recognition problems. In the end, we provide an application of a proposed similarity measure for medical diagnosis.

图片模糊集（PFS）的概念是普通模糊集和直觉模糊集的概括，其特点是正隶属度、中性隶属度和负隶属度函数。考虑到相似性度量和应用在数据挖掘、医学诊断、决策和模式识别中的重要性，文献中提出了几项研究。然而，其中一些不能满足相似性公理并提供违反直觉的情况。在本文中，我们基于两个参数t和p为PFS提出了新的相似性度量，其中t标识了不确定性级别，p是${升}_{磷}$规范。讨论了双参数相似度和距离度量的特性。我们为文献中现有的相似性度量提供了一些反例，并展示了我们提出的相似性度量如何重要并适用于模式识别问题。最后，我们提供了建议的相似性度量在医学诊断中的应用。