Journal ArticleDOI
Granular Flow Graph, Adaptive Rule Generation and Tracking
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TLDR
A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking, and it is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation.Abstract:
A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodologies formulated with superior performance. The flow graph enables in defining an intelligent technique for rule base adaptation where its characteristics in mapping the relevance of attributes and rules in decision-making system are exploited. Two new features, namely, expected flow graph and mutual dependency between flow graphs are defined to make the flow graph applicable in the tasks of both training and validation. All these techniques are performed in neighborhood granular level. A way of forming spatio-temporal 3-D granules of arbitrary shape and size is introduced. The rough flow graph-based adaptive granular rule-based system, thus produced for unsupervised video tracking, is capable of handling the uncertainties and incompleteness in frames, able to overcome the incompleteness in information that arises without initial manual interactions and in providing superior performance and gaining in computation time. The cases of partial overlapping and detecting the unpredictable changes are handled efficiently. It is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation. The quantitative indices used for evaluating the performance of tracking do not require any information on ground truth as in the other methods. Superiority of the algorithm to nonadaptive and other recent ones is demonstrated extensively.read more
Citations
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Granular computing for data analytics: a manifesto of human-centric computing
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Boosted K-nearest neighbor classifiers based on fuzzy granules
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TL;DR: Theoretical analysis and experimental results show that FGKNN and BFGKNN have better performance than that of the methods mentioned above if the appropriate parameters are given.
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Granulated deep learning and Z-numbers in motion detection and object recognition
TL;DR: The article deals with the problems of motion detection, object recognition, and scene description using deep learning in the framework of granular computing and Z-numbers and shows a balanced trade-off between speed and accuracy as compared to pixel level learning in tracking and recognition.
References
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Neighborhood rough set based heterogeneous feature subset selection
TL;DR: A neighborhood rough set model is introduced to deal with the problem of heterogeneous feature subset selection and Experimental results show that the neighborhood model based method is more flexible to deals with heterogeneous data.