Multiview action recognition has received increasing attention over the past decade. Various approaches have been proposed to extract view-invariant features; among them, self-similarity matrices (SSMs) have shown outstanding performance. However, SSMs become sensitive when there's a very large view change. To make SSMs more robust to viewpoint changes, the authors propose a collaborative sparse coding framework. They integrate the classifier training process and sparse coding process into a unified collaborative filtering framework; this lets more discriminative sparse video representations and classifiers be learned by optimizing the dictionary and classifier jointly. Experimental results demonstrate the effectiveness of the framework. © 2016 IEEE.

Collaborative Sparse Coding for Multiview Action Recognition

Wang, Wei;Yan, Yan;Sebe, Niculae
2016

Abstract

Multiview action recognition has received increasing attention over the past decade. Various approaches have been proposed to extract view-invariant features; among them, self-similarity matrices (SSMs) have shown outstanding performance. However, SSMs become sensitive when there's a very large view change. To make SSMs more robust to viewpoint changes, the authors propose a collaborative sparse coding framework. They integrate the classifier training process and sparse coding process into a unified collaborative filtering framework; this lets more discriminative sparse video representations and classifiers be learned by optimizing the dictionary and classifier jointly. Experimental results demonstrate the effectiveness of the framework. © 2016 IEEE.
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Wang, Wei; Yan, Yan; Zhang, L.; Hong, R.; Sebe, Niculae
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/166701
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