Fundamentals of pattern recognition (2nd revised and expanded ed.)
Fundamentals of pattern recognition (2nd revised and expanded ed.)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Near Sets. Special Theory about Nearness of Objects
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Information Sciences: an International Journal
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Tolerance near sets and image correspondence
International Journal of Bio-Inspired Computation
Corrigenda and addenda: tolerance near sets and image correspondence
International Journal of Bio-Inspired Computation
Perception and Classification. A Note on Near Sets and Rough Sets
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Nature-inspired framework for measuring visual image resemblance: A near rough set approach
Theoretical Computer Science
Nearness of subtly different digital images
Transactions on Rough Sets XVI
Maximal clique enumeration in finding near neighbourhoods
Transactions on Rough Sets XVI
| Hi-index | 0.00 |
The problem considered in this article stems from the observation that practical applications of near set theory requires efficient determination of all the tolerance classes containing objects from the union of two disjoints sets. Near set theory consists in extracting perceptually relevant information from groups of objects based on their descriptions. Tolerance classes are sets where all the pairs of objects within a set must satisfy the tolerance relation and the set is maximal with respect to inclusion. Finding such classes is a computationally complex problem, especially in the case of large data sets or sets of objects with similar features. The contribution of this article is a parallelized algorithm for finding tolerance classes using NVIDIA's Compute Unified Device Architecture (CUDA). The parallelized algorithm is illustrated in terms of a content-based image retrieval application.