Video Classification Using Deep Autoencoder Network

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.

Authors:
Hajati Farshid, Tavakolian Mohammad

Publication type:
A4 Article in conference proceedings

Place of publication:
Complex, Intelligent, and Software Intensive Systems. CISIS 2019

Keywords:
6G Publication

Published:

Full citation:
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

DOI:
https://doi.org/10.1007/978-3-030-22354-0_45

Read the publication here:
http://urn.fi/urn:nbn:fi-fe202102266056