@inproceedings{c642ae090953406a97771763b81c0dbf,
title = "On the evaluation of tensor-based representations for optimum-path forest classification",
abstract = "Tensor-based representations have been widely pursued in the last years due to the increasing number of high-dimensional datasets, which might be better described by the multilinear algebra. In this paper, we introduced a recent pattern recognition technique called Optimum- Path Forest (OPF) in the context of tensor-oriented applications, as well as we evaluated its robustness to space transformations using Multilinear Principal Component Analysis in both face and human action recognition tasks considering image and video datasets. We have shown OPF can obtain more accurate recognition rates in some situations when working on tensor-oriented feature spaces.",
keywords = "Gait and face recognition, Optimum-path forest, Tensors",
author = "Ricardo Lopes and Kelton Costa and Jo{\~a}o Papa",
year = "2016",
doi = "https://doi.org/10.1007/978-3-319-46182-3_10",
language = "English",
isbn = "9783319461816",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "117--125",
editor = "Friedhelm Schwenker and Abbas, {Hazem M.} and {El Gayar}, Neamat and Edmondo Trentin",
booktitle = "Artificial Neural Networks in Pattern Recognition - 7th IAPR TC3 Workshop, ANNPR 2016, Proceedings",
address = "Germany",
note = "7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016 ; Conference date: 28-09-2016 Through 30-09-2016",
}