Measuring the nearness of layered flow graphs: Application to Content Based Image Retrieval
MetadataShow full item record
Kaur, Kanwarpreet, Sheela Ramanna, and Christopher Henry. "Measuring the nearness of layered flow graphs: Application to Content Based Image Retrieval." Intelligent Decision Technologies 10(2) (February 2016):165-181. DOI: 10.3233/IDT-150246.
Rough set based flow graphs represent the flow of information for a given data set where branches of these could be constructed as decision rules. However, in the recent years, the concept of flow graphs has been applied to perceptual systems (also called perceptual flow graphs) where they play a vital role in determining the nearness among disjoint sets of perceptual objects. Perceptual flow graphs were first introduced to represent and reason about sufficiently near visual points in images. In this paper, we have given a practical implementation of flow graphs induced by a perceptual system, defined with respect to digital images, to perform Content-Based Image Retrieval(CBIR). Results are generated using the SIMPLicity dataset, and our results are compared with the near-set based tolerance nearness measure(tNM).