• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Action Recognition with deep network features and dimension reduction

Vol. 13, No. 2, February 27, 2019
10.3837/tiis.2019.02.019, Download Paper (Free):

Abstract

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.


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Cite this article

[IEEE Style]
L. Li and S. Dai, "Action Recognition with deep network features and dimension reduction," KSII Transactions on Internet and Information Systems, vol. 13, no. 2, pp. 832-854, 2019. DOI: 10.3837/tiis.2019.02.019.

[ACM Style]
Lijun Li and Shuling Dai. 2019. Action Recognition with deep network features and dimension reduction. KSII Transactions on Internet and Information Systems, 13, 2, (2019), 832-854. DOI: 10.3837/tiis.2019.02.019.

[BibTeX Style]
@article{tiis:22009, title="Action Recognition with deep network features and dimension reduction", author="Lijun Li and Shuling Dai and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2019.02.019}, volume={13}, number={2}, year="2019", month={February}, pages={832-854}}