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
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
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)
Note: A note on the problem of reporting maximal cliques
Theoretical Computer Science
A scalable, parallel algorithm for maximal clique enumeration
Journal of Parallel and Distributed Computing
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)
NEIGHBORHOOD-BASED VISION SYSTEMS
Cybernetics and Systems
Nature-inspired framework for measuring visual image resemblance: A near rough set approach
Theoretical Computer Science
Lessons learned from exploring the backtracking paradigm on the GPU
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part II
Parallel computation in finding near neighbourhoods
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Tolerance spaces: Origins, theoretical aspects and applications
Information Sciences: an International Journal
Perceptual indiscernibility, rough sets, descriptively near sets, and image analysis
Transactions on Rough Sets XV
Near Sets. Special Theory about Nearness of Objects
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
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The problem considered in this article stems from the observation that practical applications of near set theory require 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 contributions of this article are the observation that the problem of finding tolerance classes is equivalent to the MCE problem, empirical evidence verifying the conjecture from [15] that the extra perceptual information obtained by finding all tolerance classes on a set of objects obtained from a pair of images improves the CBIR results when using the tolerance nearness measure, and a new application of MCE to CBIR.