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2017 | OriginalPaper | Buchkapitel

Action Graph Decomposition Based on Sparse Coding

verfasst von : Wengang Feng, Huawei Tian, Yanhui Xiao, Jianwei Ding, Yunqi Tang

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

A video can be thought of as a visual document which may be represented from different dimensions such as frames, objects and other different levels of features. Action recognition is usually one of the most important and popular tasks, and requires the understanding of temporal and spatial cues in videos. What structures do the temporal relationships share in common inter- and intra-classes of actions? What is the best representation for those temporal relationships? We propose a new temporal relationship representation, called action graphs based on Laplacian matrices and Allen’s temporal relationships. Recognition framework based on sparse coding, which also mimics human vision system to represent and infer knowledge. To our best knowledge, “action graphs” is put forward to represent the temporal relationships. we are the first using sparse graph coding for event analysis.

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Titel: Action Graph Decomposition Based on Sparse Coding
verfasst von: Wengang Feng
Huawei Tian
Yanhui Xiao
Jianwei Ding
Yunqi Tang
Verlag: Springer International Publishing
Buch: Image and Graphics
Print ISBN: 978-3-319-71606-0

Electronic ISBN: 978-3-319-71607-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-71607-7_5

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