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Authors: Christina Chrysouli ; Vasileios Gavriilidis and Anastasios Tefas

Affiliation: Aristotle University of Thessaloniki, Greece

ISBN: 978-989-758-054-3

Keyword(s): Activity Learning, Random Walk Kernel, Support Vector Machines, Feature Extraction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Support Vector Machines and Applications ; Theory and Methods

Abstract: In this paper, we propose a novel graph-based kernel method in order to construct histograms for a bag of words approach, by using similarity measures, applied in activity recognition problems. Bag of words is the most popular framework for performing classification on video data. This framework, however, is an orderless collection of features. We propose a better way to encode action in videos, via altering the histograms. The creation of such histograms is performed based on kernel methods, inspired from graph theory, computed with no great additional computational cost. Moreover, when using the proposed algorithm to construct the histograms, a richer representation of videos is attained. Experiments on folk dances recognition were conducted based on our proposed method, by comparing histograms extracted from a typical bag-of-words framework against histograms of the proposed method, which provided promising results on this challenging task.

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Paper citation in several formats:
Chrysouli, C.; Gavriilidis, V. and Tefas, A. (2014). Graph-based Kernel Representation of Videos for Traditional Dance Recognition.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 195-202. DOI: 10.5220/0005076101950202

@conference{ncta14,
author={Christina Chrysouli. and Vasileios Gavriilidis. and Anastasios Tefas.},
title={Graph-based Kernel Representation of Videos for Traditional Dance Recognition},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={195-202},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005076101950202},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Graph-based Kernel Representation of Videos for Traditional Dance Recognition
SN - 978-989-758-054-3
AU - Chrysouli, C.
AU - Gavriilidis, V.
AU - Tefas, A.
PY - 2014
SP - 195
EP - 202
DO - 10.5220/0005076101950202

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