Evaluating Manifold Learning Methods and Discriminative Sequence Classifiers in View-Invariant Action Recognition

2011 ◽  
pp. 11-18
Author(s):  
Rodrigo Cilla ◽  
Miguel A. Patricio ◽  
Antonio Berlanga ◽  
José M. Molina
Keyword(s):  
Action Recognition ◽  
Manifold Learning ◽  
Learning Methods ◽  
Invariant Action

View invariant action recognition using projective depth

2014 ◽  
Vol 123 ◽  
pp. 41-52 ◽  
Author(s):  
Nazim Ashraf ◽  
Chuan Sun ◽  
Hassan Foroosh
Keyword(s):  
Action Recognition ◽  
Invariant Action

Learning Manifolds

2012 ◽  
pp. 374-402
Author(s):  
Diana Mateus ◽  
Christian Wachinger ◽  
Selen Atasoy ◽  
Loren Schwarz ◽  
Nassir Navab
Keyword(s):  
Manifold Learning ◽  
Domain Knowledge ◽  
Dimensional Space ◽  
Human Motion ◽  
Motion Modeling ◽  
Learning Methods ◽  
Data Representations ◽  
Non Linear ◽  
Data Points ◽  
Low Dimensional

Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. One alternative to deal with such data is dimensionality reduction. This chapter focuses on manifold learning methods to create low dimensional data representations adapted to a given application. From pairwise non-linear relations between neighboring data-points, manifold learning algorithms first approximate the low dimensional manifold where data lives with a graph; then, they find a non-linear map to embed this graph into a low dimensional space. Since the explicit pairwise relations and the neighborhood system can be designed according to the application, manifold learning methods are very flexible and allow easy incorporation of domain knowledge. The authors describe different assumptions and design elements that are crucial to building successful low dimensional data representations with manifold learning for a variety of applications. In particular, they discuss examples for visualization, clustering, classification, registration, and human-motion modeling.

Attention Transfer (ANT) Network for View-invariant Action Recognition

2019 ◽  
Author(s):  
Yanli Ji ◽  
Feixiang Xu ◽  
Yang Yang ◽  
Ning Xie ◽  
Heng Tao Shen ◽  
...  
Keyword(s):  
Action Recognition ◽  
Invariant Action

Survey on deep learning methods in human action recognition

2017 ◽  
Vol 11 (8) ◽  
pp. 623-632 ◽  
Author(s):  
Maryam Koohzadi ◽  
Nasrollah Moghadam Charkari
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Learning Methods
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