@misc{10481/70404,
year = {2021},
month = {8},
url = {http://hdl.handle.net/10481/70404},
abstract = {Combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) produces a powerful architecture
for video classification problems as spatial–temporal information can be processed simultaneously and effectively. Using
transfer learning, this paper presents a comparative study to investigate how temporal information can be utilized to
improve the performance of video classification when CNNs and RNNs are combined in various architectures. To enhance
the performance of the identified architecture for effective combination of CNN and RNN, a novel action template-based
keyframe extraction method is proposed by identifying the informative region of each frame and selecting keyframes based
on the similarity between those regions. Extensive experiments on KTH and UCF-101 datasets with ConvLSTM-based
video classifiers have been conducted. Experimental results are evaluated using one-way analysis of variance, which
reveals the effectiveness of the proposed keyframe extraction method in the sense that it can significantly improve video
classification accuracy.},
organization = {Ministry of National Education - Turkey},
organization = {Spanish Ministry of Science, Innovation, and Universities	PGC2018-098813-B-C31},
organization = {ERDF fund},
publisher = {Springer},
keywords = {Deep learning},
keywords = {Convolutional neural networks},
keywords = {Recurrent neural networks},
keywords = {Keyframe extraction},
keywords = {Video classification},
title = {A novel keyframe extraction method for video classification using deep neural networks},
doi = {10.1007/s00521-021-06322-x},
author = {Kiziltepe, Rukiye Savran and Gan, John Q. and Escobar Pérez, Juan José},
}