Discovery of Patterns in Associated Sets: Foundations and Applications

关联集中模式的发现：基础和应用

• 批准号: 194376-2012
• 负责人: Ramanna, Sheela
• 金额: $ 1.02万
• 依托单位: University of Winnipeg
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=569774
• 关键词: Discovery; Patterns; Associated; Sets; Foundations

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)? A direct result of this research is its utility in a number of application areas such as visual surveillance, image authentication, trustworthiness of multimedia websites, product association displays in retailing, in education, and change detection and tile clustering in remotely sensed images from satellite and airborne camera systems. The novelty of the proposed research is in (i) finding patterns based on nearness (or apartness) of members of associated sets that are compared descriptively, (ii) using various metrics that measure the distance between sets to determine the degree of nearness or apartness of visual sets with high acuity and (iii) introducing feature vector-based distance functions to discern fine-grained nearness to (apartness from) members of associated sets.

拟议的研究引入了一个计算框架来测量视觉信息颗粒之间的相似性。利用相关的集合和特征向量的接近距离泛函，可以设计支持近似推理的基于感知的视觉系统（特征向量提供集合成员的描述）。这种方法导致信息颗粒可视化，因为这样的集合有利于描述-以及在人工视觉系统的设计中基于位置的事件检测以及广泛的技术转让。建议的研究的科学方法是发展的基础和应用程序的关联集识别的不相交的视觉对象集的成员之间的描述的亲和力。所提出的接近度量化方法是通过一个相关联的集合框架来实现的，该框架使用各种距离函数来识别相对于集合之间的距离具有相似性（或不相似性）的非空集合。换句话说，我们正在寻找问题的答案：一对集合足够近（足够远），可以被认为相似（或不相似）吗？ 这项研究的一个直接结果是它的实用程序在一些应用领域，如视觉监控，图像认证，多媒体网站的可信度，产品协会显示在零售业，教育，变化检测和瓷砖聚类遥感图像从卫星和机载相机系统。所提出的研究的新奇在于（i）基于接近度来寻找模式（或apartness）相关联的集合的成员进行比较，（ii）使用测量集合之间的距离的各种度量来确定具有高敏锐度的视觉集合的接近度或分离度，以及（iii）引入基于特征向量的距离函数来辨别细粒度的接近度，（与）相关集合的成员（分离）。