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Soft Computing

Erschienen in:

01.01.2015 | Focus

NestDE: generic parameters tuning for automatic story segmentation

verfasst von: Wei Feng, Xuefei Yin, Yifeng Zhang, Lei Xie

Erschienen in: Soft Computing | Ausgabe 1/2015

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Abstract

Parameters tuning is a crucial task in automatic story segmentation. For most previous story segmentation methods, however, the parameters were simply derived from empirical tuning, which may indeed harm the fairness of the evaluation, or even misguide the conclusion. In this paper, we present a general parameters tuning approach, namely nested differential evolution. As a practical general-purpose parameters tuner, our approach itself is parameters-robust and is generic enough to optimize the most usual types of parameters for the given corpus and evaluation criterion. Besides, our approach is able to cooperate with empirical tuning and jointly produce better parameters based on the prior knowledge of experienced users. Extensive experiments on synthetic challenging quadratic pseudo-Boolean optimization and real-world story segmentation tasks validate the superior performance of our approach over traditional empirical tuning and other generic optimizers, such as simulated annealing and classical differential evolution.

Vorheriger Artikel Topic segmentation on spoken documents using self-validated acoustic cuts

Nächster Artikel Learning topic of dynamic scene using belief propagation and weighted visual words approach

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Parameters tuning is not only important in story segmentation, in low-level vision (Feng and Liu 2008; Feng et al. 2010, 2013), but parameters are also crucial for an algorithm to obtain good performance.

To facilitate parameters tuning, besides raw data, the tuning corpus \(\mathbb {C}\) should also contain ground truth labeling.

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Titel: NestDE: generic parameters tuning for automatic story segmentation
verfasst von: Wei Feng
Xuefei Yin
Yifeng Zhang
Lei Xie
Publikationsdatum: 01.01.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 1/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1450-2

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