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Authors: S. Molina-Giraldo ; J. Carvajal-González ; A. M. Álvarez-Meza and G. Castellanos-Domínguez

Affiliation: Universidad Nacional de Colombia, Colombia

Keyword(s): Background Subtraction, Multiple Kernel Learning, Relevance Analysis, Data Separability.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Object Recognition ; Pattern Recognition ; Software Engineering ; Video Analysis

Abstract: A methodology to automatically detect moving objects in a scene using static cameras is proposed. Using Multiple Kernel Representations, we aim to incorporate multiple information sources in the process, and employing a relevance analysis, each source is automatically weighted. A tuned Kmeans technique is employed to group pixels as static or moving objects. Moreover, the proposed methodology is tested for the classification of abbandoned objects. Attained results over real-world datasets, show how our approach is stable using the same parameters for all experiments.

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Paper citation in several formats:
Molina-Giraldo, S.; Carvajal-González, J.; M. Álvarez-Meza, A. and Castellanos-Domínguez, G. (2013). Video Segmentation based on Multi-kernel Learning and Feature Relevance Analysis for Object Classification. In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-8565-41-9; ISSN 2184-4313, SciTePress, pages 396-401. DOI: 10.5220/0004269403960401

@conference{icpram13,
author={S. Molina{-}Giraldo. and J. Carvajal{-}González. and A. {M. Álvarez{-}Meza}. and G. Castellanos{-}Domínguez.},
title={Video Segmentation based on Multi-kernel Learning and Feature Relevance Analysis for Object Classification},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2013},
pages={396-401},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004269403960401},
isbn={978-989-8565-41-9},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Video Segmentation based on Multi-kernel Learning and Feature Relevance Analysis for Object Classification
SN - 978-989-8565-41-9
IS - 2184-4313
AU - Molina-Giraldo, S.
AU - Carvajal-González, J.
AU - M. Álvarez-Meza, A.
AU - Castellanos-Domínguez, G.
PY - 2013
SP - 396
EP - 401
DO - 10.5220/0004269403960401
PB - SciTePress

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