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On the use of MKL for cooking action recognition

机译：关于使用MKL进行烹饪动作识别

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摘要

Automatic action recognition in videos is a challenging computer vision task that has become an active research area in recent years. Existing strategies usually use kernel-based learning algorithms that considers a simple combination of different features completely disregarding how such features should be integrated to fit the given problem. Since a given feature is most suitable to describe a given image/video property, the adaptive weighting of such features can improve the performance of the learning algorithm. In this paper, we investigated the use of the Multiple Kernel Learning (MKL) algorithm to adaptive search for the best linear relation among the considered features. MKL is an extension of the support vector machines (SVMs) to work with a weighted linear combination of several single kernels. This approach allows to simultaneously estimate the weights for the multiple kernels combination as well as the underlying SVM parameters. In order to prove the validity of the MKL approach, we considered a descriptor composed of multiple features aligned with dense trajectories. We experimented our approach on a database containing 36 cooking actions. Results confirm that the use of MKL improves the classification performance.
机译：视频中的自动动作识别是一项具有挑战性的计算机视觉任务，近年来已成为活跃的研究领域。现有策略通常使用基于内核的学习算法，该算法考虑了不同功能的简单组合，而完全忽略了应如何集成这些功能以适应给定的问题。由于给定的特征最适合描述给定的图像/视频属性，因此这些特征的自适应加权可以提高学习算法的性能。在本文中，我们研究了使用多核学习（MKL）算法自适应搜索所考虑特征之间的最佳线性关系。 MKL是支持向量机（SVM）的扩展，可以与几个单个内核的加权线性组合一起工作。这种方法允许同时估计多个内核组合的权重以及基础SVM参数。为了证明MKL方法的有效性，我们考虑了一个描述符，该描述符由与密集轨迹对齐的多个特征组成。我们对包含36个烹饪动作的数据库进行了实验。结果证实，使用MKL可以提高分类性能。

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