Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Distribution-based Image Similarity
State-of-the-Art in Content-Based Image and Video Retrieval [Dagstuhl Seminar, 5-10 December 1999]
On the coverings by tolerance classes
Information Sciences—Informatics and Computer Science: An International Journal
Reinforcement Learning in Swarms that Learn
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Reinforcement Learning with Approximation Spaces
Fundamenta Informaticae
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
Rough Fuzzy Image Analysis: Foundations and Methodologies
Rough Fuzzy Image Analysis: Foundations and Methodologies
Tolerance near sets and image correspondence
International Journal of Bio-Inspired Computation
Nearness of subtly different digital images
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Perceptual indiscernibility, rough sets, descriptively near sets, and image analysis
Transactions on Rough Sets XV
Nearness of subtly different digital images
Transactions on Rough Sets XVI
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The problem considered in this article is how to detect and measure resemblances between swarm behaviours. The solution to this problem stems from an extension of recent work on tolerance near sets and image correspondence. Instead of considering feature extraction from subimages in digital images, we compare swarm behaviours by considering feature extraction from subsets of tuples of feature-values representing the behaviour of observed swarms of organisms. Thanks to recent work on the foundations of near sets, it is possible to formulate a rigorous approach to measuring the extent that swarm behaviours resemble each other. Fundamental to this approach is what is known as a recent description-based set intersection, a set containing objects with matching or almost the same descriptions extracted from objects contained in pairs of disjoint sets. Implicit in this work is a new approach to comparing information tables representing N. Tinbergen's ethology (study of animal behaviour) and direct result of recent work on what is known as rough ethology. Included in this article is a comparison of recent nearness measures that includes a new form of F. Hausdorff's distance measure. The contribution of this article is a tolerance near set approach to measuring the degree of resemblance between swarm behaviours.