Video classification using deep autoencoder network

Hajati, Farshid; Tavakolian, Mohammad (2020-06-21)
 
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https://doi.org/10.1007/978-3-030-22354-0_45

Hajati, Farshid
Tavakolian, Mohammad
Springer Nature
21.06.2020

Hajati F., Tavakolian M. (2020) Video Classification Using Deep Autoencoder Network. In: Barolli L., Hussain F., Ikeda M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2019. Advances in Intelligent Systems and Computing, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-22354-0_45

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© Springer Nature Switzerland AG 2020. This is a post-peer-review, pre-copyedit version of an article published in Complex, Intelligent, and Software Intensive Systems. CISIS 2019. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-22354-0_45.
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doi:https://doi.org/10.1007/978-3-030-22354-0_45
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https://urn.fi/URN:NBN:fi-fe202102266056
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Abstract

We present a deep learning framework for video classification applicable to face recognition and dynamic texture recognition. A Deep Autoencoder Network Template (DANT) is designed whose weights are initialized by conducting unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines. In order to obtain a class specific network and fine tune the weights for each class, the pre-initialized DANT is trained for each class of video sequences, separately. A majority voting technique based on the reconstruction error is employed for the classification task. The extensive evaluation and comparisons with state-of-the-art approaches on Honda/UCSD, DynTex, and YUPPEN databases demonstrate that the proposed method significantly improves the performance of dynamic texture classification.

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