Efficient descriptor tree growing for fast action recognition

Video and image classification based on Instance-to-Class (I2C) distance attracted many recent studies, due to the good generalization capabilities it provides for non-parametric classifiers. In this work we propose a method for action recognition. Our approach needs no intensive learning stage, and...

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Detalles Bibliográficos
Publicado: 2014
Materias:
Action recognition
Instance-to-Class distance
Nearest neighbor
Pattern recognition
Software engineering
Class-distance
Classification performance
Generalization capability
Nearest neighbors
Non-parametric classifiers
State of the art
Training database
Classification (of information)
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01678655_v36_n1_p213_Ubalde
http://hdl.handle.net/20.500.12110/paper_01678655_v36_n1_p213_Ubalde
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_01678655_v36_n1_p213_Ubalde
record_format dspace
spelling paper:paper_01678655_v36_n1_p213_Ubalde2023-06-08T15:17:00Z Efficient descriptor tree growing for fast action recognition Action recognition Instance-to-Class distance Nearest neighbor Pattern recognition Software engineering Action recognition Class-distance Classification performance Generalization capability Nearest neighbors Non-parametric classifiers State of the art Training database Classification (of information) Video and image classification based on Instance-to-Class (I2C) distance attracted many recent studies, due to the good generalization capabilities it provides for non-parametric classifiers. In this work we propose a method for action recognition. Our approach needs no intensive learning stage, and its classification performance is comparable to the state-of-the-art. A smart organization of training data allows the classifier to achieve reasonable computation times when working with large training databases. An efficient method for organizing training data in such a way is proposed. We perform thorough experiments on two popular action recognition datasets: the KTH dataset and the IXMAS dataset, and we study the influence of one of the key parameters of the method on classification performance. © 2013 Elsevier B.V. All rights reserved. 2014 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01678655_v36_n1_p213_Ubalde http://hdl.handle.net/20.500.12110/paper_01678655_v36_n1_p213_Ubalde
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Action recognition
Instance-to-Class distance
Nearest neighbor
Pattern recognition
Software engineering
Action recognition
Class-distance
Classification performance
Generalization capability
Nearest neighbors
Non-parametric classifiers
State of the art
Training database
Classification (of information)
spellingShingle Action recognition
Instance-to-Class distance
Nearest neighbor
Pattern recognition
Software engineering
Action recognition
Class-distance
Classification performance
Generalization capability
Nearest neighbors
Non-parametric classifiers
State of the art
Training database
Classification (of information)
Efficient descriptor tree growing for fast action recognition
topic_facet Action recognition
Instance-to-Class distance
Nearest neighbor
Pattern recognition
Software engineering
Action recognition
Class-distance
Classification performance
Generalization capability
Nearest neighbors
Non-parametric classifiers
State of the art
Training database
Classification (of information)
description Video and image classification based on Instance-to-Class (I2C) distance attracted many recent studies, due to the good generalization capabilities it provides for non-parametric classifiers. In this work we propose a method for action recognition. Our approach needs no intensive learning stage, and its classification performance is comparable to the state-of-the-art. A smart organization of training data allows the classifier to achieve reasonable computation times when working with large training databases. An efficient method for organizing training data in such a way is proposed. We perform thorough experiments on two popular action recognition datasets: the KTH dataset and the IXMAS dataset, and we study the influence of one of the key parameters of the method on classification performance. © 2013 Elsevier B.V. All rights reserved.
title Efficient descriptor tree growing for fast action recognition
title_short Efficient descriptor tree growing for fast action recognition
title_full Efficient descriptor tree growing for fast action recognition
title_fullStr Efficient descriptor tree growing for fast action recognition
title_full_unstemmed Efficient descriptor tree growing for fast action recognition
title_sort efficient descriptor tree growing for fast action recognition
publishDate 2014
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01678655_v36_n1_p213_Ubalde
http://hdl.handle.net/20.500.12110/paper_01678655_v36_n1_p213_Ubalde
_version_ 1768542733876592640

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