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2016 | OriginalPaper | Buchkapitel

Video Event Detection Using Kernel Support Vector Machine with Isotropic Gaussian Sample Uncertainty (KSVM-iGSU)

verfasst von : Christos Tzelepis, Vasileios Mezaris, Ioannis Patras

Erschienen in: MultiMedia Modeling

Verlag: Springer International Publishing

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Abstract

In this paper, we propose an algorithm that learns from uncertain data and exploits related videos for the problem of event detection; related videos are those that are closely associated, though not fully depicting the event of interest. In particular, two extensions of the linear SVM with Gaussian Sample Uncertainty are presented, which (a) lead to non-linear decision boundaries and (b) incorporate related class samples in the optimization problem. The resulting learning methods are especially useful in problems where only a limited number of positive and related training observations are provided, e.g., for the 10Ex subtask of TRECVID MED, where only ten positive and five related samples are provided for the training of a complex event detector. Experimental results on the TRECVID MED 2014 dataset verify the effectiveness of the proposed methods.

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Nächstes Kapitel Video Content Representation Using Recurring Regions Detection
Fußnoten
1
\(\mathbb {S}_{++}^{n}\) denotes the convex cone of all symmetric positive definite \(n\times n\) matrices with entries in \(\mathbb {R}\). \(I_n\) denotes the identity matrix of order n.
 
2
Convexity can be shown using Theorem 2 proved in [27].
 
3
Their derivation is omitted, as it is technical but straightforward.
 
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Metadaten
Titel
Video Event Detection Using Kernel Support Vector Machine with Isotropic Gaussian Sample Uncertainty (KSVM-iGSU)
verfasst von
Christos Tzelepis
Vasileios Mezaris
Ioannis Patras
Copyright-Jahr
2016
Buch
MultiMedia Modeling

Print ISBN: 978-3-319-27670-0

Electronic ISBN: 978-3-319-27671-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-27671-7_1

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