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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >Video Highlight Detection via Region-Based Deep Ranking Model
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Video Highlight Detection via Region-Based Deep Ranking Model

机译：通过基于区域的深度排名模型进行视频精彩片段检测

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摘要

The video highlight detection task is to localize key elements (moments of user's major or special interest) in a video. Most of the existing highlight detection approaches extract features from the video segment as a whole without considering the difference of local features spatially. In spatial extent, not all regions are worth watching because some of them only contain the background of the environment without human or other moving objects, especially when there is lots of clutter in the background. To deal with this issue, we propose a novel region-based model which can automatically localize the key elements in a video without any extra supervised annotations. Specifically, the proposed model produces position-sensitive score maps for local regions in the spatial dimension of the video segment, and then aggregates all position-wise scores with position-pooling operation. The regions with higher response values will be extracted as key elements. Thus more effective features of the video segment are obtained to predict the highlight score. The proposed position-sensitive scheme can be easily integrated into an endto-end fully convolutional network which aims to update parameters via stochastic gradient descent method in the backward propagation to improve the robustness of the model. Extensive experimental results on the YouTube and SumMe datasets demonstrate that the proposed approach achieves significant improvement over state-of-the-art methods.
机译：视频精彩片段检测任务是定位视频中的关键元素（用户的主要兴趣或特殊兴趣的时刻）。大多数现有的高光检测方法从整个视频片段中提取特征，而不考虑空间上局部特征的差异。在空间范围内，并不是所有区域都值得一看，因为其中一些区域仅包含环境背景而没有人或其他移动物体，尤其是在背景中杂乱无章的情况下。为了解决这个问题，我们提出了一种新颖的基于区域的模型，该模型可以自动定位视频中的关键元素，而无需任何额外的监督注释。具体而言，提出的模型为视频片段的空间维度中的局部区域生成位置敏感得分图，然后使用位置合并操作汇总所有位置得分。具有较高响应值的区域将被提取为关键元素。因此，获得了视频片段的更有效特征来预测精彩片段。所提出的位置敏感方案可以容易地集成到端到端全卷积网络中，该网络旨在通过随机梯度下降法在向后传播中更新参数，以提高模型的鲁棒性。 YouTube和SumMe数据集上的大量实验结果表明，所提出的方法相对于最新方法取得了显着改进。

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