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Multi-modal learning for affective content analysis in movies

机译：电影中情感内容分析的多模态学习

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摘要

Affective content analysis is an important research topic in video content analysis, and has extensive applications in many fields. However, it is a challenging task to design a computational model for predicting emotions induced by videos, since the elicited emotions can be considered relatively subjective. Intuitively, several features of different modalities can depict the elicited emotions, but the correlation and influence of these features are still not well studied. To address this issue, we propose a multi-modal learning framework, which classifies affective contents in the valence-arousal space. In particular, we utilize the features extracted by the methods of motion keypoint trajectory and convolutional neural networks to depict the visual modality of elicited emotions, and extract a global audio feature by the openSMILE toolkit to describe the audio modality. Then, the linear support vector machine and support vector regression are employed to learn the affective models. By comparing these three features with five baseline features, we discover that the three features are significant for describing affective content. Experimental results also demonstrate that the three features complement each other. Moreover, the proposed framework obtains the state-of-the-art results on two challenging datasets of video affective content analysis.
机译：情感内容分析是视频内容分析中的重要研究主题，在许多领域具有广泛的应用。然而，设计用于预测视频引起的情绪的计算模型是一个具有挑战性的任务，因为引发的情绪可以被认为是相对主观的。直观地，不同方式的若干特征可以描绘出引发的情绪，但这些特征的相关性和影响仍然没有很好地研究。为了解决这个问题，我们提出了一个多模态学习框架，将情感内容分类在价值 - 唤醒空间中。特别地，我们利用由运动键盘轨迹轨迹和卷积神经网络的方法提取的特征来描绘引发情绪的视觉模型，并通过开放式工具包提取全局音频特征来描述音频模态。然后，采用线性支持向量机和支持向量回归来学习情感模型。通过将这三个特征与五个基线特征进行比较，我们发现三个特征对于描述情感内容很重要。实验结果还证明了三个特征彼此相互补充。此外，所提出的框架获得了最先进的视频情感内容分析的两个具有挑战性的数据集。

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