@inproceedings{0707eee7a6974704a53b34009abb7a5f,
title = "Human action recognition based on bag of features and multi-view neural networks",
abstract = "In this paper, we employ Single-hidden Layer Feedforward Neural networks in order to perform human action recognition based on multiple action representations. In order to determine both optimized network and action representation combination weights, we propose an optimization process that jointly minimizes the overall network training error and the within-class variance of the training data in the corresponding hidden layer spaces. The proposed approach has been evaluated by using the state-of-the-art Bag of Features-based action video representation on three publicly available action recognition databases, where it outperforms two commonly used video representation combination approaches, as well as the best single-descriptor classification outcome.",
keywords = "Single-hidden Layer Feedforward Neural networks, Multi-view Learning, Human Action Recognition, Bag of Features",
author = "Alexandros Iosifidis and Anastasios Tefas and Ioannis Pitas",
year = "2015",
month = mar,
doi = "10.1109/ICIP.2014.7025302",
language = "English",
isbn = "9781479957521",
series = "IEEE International Conference on Image Processing (ICIP)",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "1510--1514",
booktitle = "2014 IEEE International Conference on Image Processing (ICIP 2014)",
address = "United States",
note = "IEEE International Conference on Image Processing (ICIP) ; Conference date: 27-10-2014 Through 30-10-2014",
}