Discovery of Patterns in Associated Sets: Foundations and Applications

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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)?