Attribute Characteristics of Object (Property) Oriented Concept Lattices

2012 ◽  
pp. 341-348
Author(s):  
Ling Wei ◽  
Min-Qian Liu
Keyword(s):  
Concept Lattices ◽  
Object Property

scholarly journals Gradation of Fuzzy Preconcept Lattices

Axioms ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 41
Author(s):  
Alexander Šostak ◽  
Ingrīda Uļjane ◽  
Māris Krastiņš
Keyword(s):  
Fuzzy Information ◽  
Concept Lattices ◽  
Practical Applications ◽  
Fuzzy Concept ◽  
The One ◽  
Practical Nature

Noticing certain limitations of concept lattices in the fuzzy context, especially in view of their practical applications, in this paper, we propose a more general approach based on what we call graded fuzzy preconcept lattices. We believe that this approach is more adequate for dealing with fuzzy information then the one based on fuzzy concept lattices. We consider two possible gradation methods of fuzzy preconcept lattice—an inner one, called D-gradation and an outer one, called M-gradation, study their properties, and illustrate by a series of examples, in particular, of practical nature.

On context patterns associated with concept lattices

Order ◽  
1993 ◽  
Vol 10 (4) ◽  
pp. 363-373
Author(s):  
Winfried Geyer
Keyword(s):  
Concept Lattices

On multi-adjoint concept lattices based on heterogeneous conjunctors

2012 ◽  
Vol 208 ◽  
pp. 95-110 ◽  
Author(s):  
J. Medina ◽  
M. Ojeda-Aciego
Keyword(s):  
Concept Lattices

On the use of thresholds in multi-adjoint concept lattices

2014 ◽  
Vol 92 (9) ◽  
pp. 1855-1873 ◽  
Author(s):  
M. Eugenia Cornejo ◽  
Jesús Medina ◽  
Eloisa Ramírez-Poussa
Keyword(s):  
Concept Lattices

Distributive laws for concept lattices

1993 ◽  
Vol 30 (4) ◽  
pp. 538-580 ◽  
Author(s):  
Marcel Ern�
Keyword(s):  
Concept Lattices

scholarly journals Inferring object properties from human interaction and transferring them to new motions

2021 ◽  
Author(s):  
Qian Zheng ◽  
Weikai Wu ◽  
Hanting Pan ◽  
Niloy Mitra ◽  
Daniel Cohen-Or ◽  
...  
Keyword(s):  
Human Interaction ◽  
Strongly Correlated ◽  
Recognition Method ◽  
Fine Grained ◽  
Object Property ◽  
Correlated Motions ◽  
Object Properties ◽  
New Synthesis ◽  
Related Object ◽  
Highly Correlated

AbstractHumans regularly interact with their surrounding objects. Such interactions often result in strongly correlated motions between humans and the interacting objects. We thus ask: “Is it possible to infer object properties from skeletal motion alone, even without seeing the interacting object itself?” In this paper, we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone. This inference allows us to disentangle the motion from the object property and transfer object properties to a given motion. We collected a large number of videos and 3D skeletal motions of performing actors using an inertial motion capture device. We analyzed similar actions and learned subtle differences between them to reveal latent properties of the interacting objects. In particular, we learned to identify the interacting object, by estimating its weight, or its spillability. Our results clearly demonstrate that motions and interacting objects are highly correlated and that related object latent properties can be inferred from 3D skeleton sequences alone, leading to new synthesis possibilities for motions involving human interaction. Our dataset is available at http://vcc.szu.edu.cn/research/2020/IT.html.

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