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Fixation Prediction in Videos Using Unsupervised Hierarchical Features

机译：使用无监督分层功能的视频中的固定预测

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摘要

This paper presents a framework for saliency estimation and fixation prediction in videos. The proposed framework is based on a hierarchical feature representation obtained by stacking convolutional layers of independent subspace analysis (ISA) filters. The feature learning is thus unsupervised and independent of the task. To compute the saliency, we then employ a multiresolution saliency architecture that exploits both local and global saliency. That is, for a given image, an image pyramid is initially built. After that, for each resolution, both local and global saliency measures are computed to obtain a saliency map. The integration of saliency maps over the image pyramid provides the final video saliency. We first show that combining local and global saliency improves the results. We then compare the proposed model with several video saliency models and demonstrate that the proposed framework is capable of predicting video saliency effectively, outperforming all the other models.
机译：本文介绍了视频中显着估计和固定预测的框架。所提出的框架基于通过堆叠独立子空间分析（ISA）滤波器的卷积层获得的分层特征表示。因此，特征学习是无监督和独立于任务。要计算显着性，我们采用了一种利用本地和全局显着性的多分辨率显着架构。也就是说，对于给定图像，最初构建了图像金字塔。之后，对于每个分辨率，计算本地和全局显着性能措施以获得显着图。显着图在图像金字塔上积分提供最终的视频显着性。我们首先表明，结合本地和全球显着性提高了结果。然后，我们将提出的模型与多个视频显着模型进行比较，并证明所提出的框架能够有效地预测视频显着性，优于所有其他模型。

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