Discovery of Patterns in Associated Sets: Foundations and Applications

提出的研究引入了一个计算框架来衡量视觉信息颗粒之间的相似性。利用关联集合和特征向量的贴近距离泛函，可以设计支持近似推理的基于感知的视觉系统(特征向量提供对集合成员的描述)。这种方法导致了信息颗粒可视化，因为这样的集合促进了人工视觉系统设计中基于描述和基于位置的事件检测以及基础广泛的技术转让。提出的研究的科学方法是发展关联集合的基础和应用，以辨别不相交的视觉对象集合的成员的描述之间的亲和力。所提出的贴近度量化方法是通过关联集合框架来实现的，该关联集合框架使用各种距离函数来辨别相对于集合之间的距离描述性地相似(或不相似)的非空集。换句话说，我们正在寻找这个问题的答案：一对集合是否足够近(足够远)而被视为相似(或不相似)？

The proposed research introduces a computational framework for measuring resemblance between visual information granules. The utilization of associated sets and nearness distance functionals of feature vectors make it possible to design perception-based vision systems that support approximate reasoning (feature vectors provide descriptions of set members). This approach leads to information granule visualisation because such sets facilitate description- as well as location-based event detection in the design of artificial vision systems as well as broad-based technology transfer. The scientific approach of the proposed research is to develop the foundations and applications of associated sets in discerning affinities between descriptions of members of disjoint sets of visual objects. The proposed nearness quantification approach is accomplished via an associated set framework in discerning non-empty sets that are descriptively similar (or dissimilar) relative to the distances between the sets using various distance functions. In other words, we are seeking answers to the question: Is a pair of sets sufficiently near (sufficiently far apart) to be considered similar (or dissimilar)?