Journal
OPTIK
Volume 126, Issue 9-10, Pages 882-887Publisher
ELSEVIER GMBH
DOI: 10.1016/j.ijleo.2015.02.053
Keywords
Compressive sensing; Dimensionality reduction; Action recognition
Categories
Funding
- NSAF [11176018]
- National Natural Science Foundation of China [61471248]
- Robot Technology Used for Special Environment Key Laboratory of Sichuan Province [14zxtk03]
- Promotive Research Fund for Young Middle-aged Scientists of Shandong Province [BS2014DX009]
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We propose a new dimensionality reduction method called compressive sensing with Gaussian mixture random matrix (CS-GMRM), in which a novel measurement matrix using Gaussian mixture distribution is constructed and is proved to satisfy the restricted isometry property. The CS-GMRM method projects high-dimensional vector spaces into low-dimensional ones via a single matrix multiplication. In particular, the proposed method removes the need of a training process, preserves the metric information of the original vector space, and requires a low level of computational complexity. We apply our method to the problem of recognizing human action from video sequences. Experimental results show that the proposed method is simultaneously highly effective and highly efficient for action recognition, and outperforms the state-of-the-art dimensionality reduction methods. (C) 2015 Elsevier GmbH. All rights reserved.
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