Visual Violence Rating with Pairwise Comparison
Visual Violence Rating with Pairwise Comparison
复制标题
视觉暴力评级与成对比较
DOI：
10.1109/icip.2019.8803573
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Katoy Jien
中科院分区：
文献类型：
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作者：
Ji Ying;Wang Yu;Katoy Jien
随着互联网的迅速发展，儿童遭受暴力已成为一个严峻的问题。识别暴力视频和估计暴力程度变得至关重要。大多数研究集中在暴力场景或暴力行为检测，缺乏整体的暴力程度信息。在本文中，我们提出了一种暴力评级预测方法，并建立了一个新的暴力视频数据集。我们提出的方法有两个优点：（1）视频是由从学习的双流网络中提取的特征表示的;（2）不同暴力程度之间的关系可以学习并用于预测暴力等级。为了证明我们的方法的有效性，我们创建了一个包含1930个暴力视频的标记良好的数据集。每个视频都有6个客观暴力属性。此外，我们采用成对比较的方法来获得地面实况暴力评级为每个视频。我们提出的方法在我们的数据集上进行了评估。其结果表明，我们提出的方法优于最先进的视频分类方法。
Children's exposure to violence has become a severe problem with the rapid development of Internet. Recognizing violent video and estimating violence extent become crucial. Most researches focus on violent scene or violent action detection, lacking overall violence extent information. In this paper, we propose a violence rating prediction approach and build a novel violent video dataset. Our proposed method has two advantages: (1) videos are represented by features extracted from a learned two-stream network; (2) relationship between different violence extent can be learned and utilized to predict violence rating. To demonstrate the effectiveness of our method, we created a well-labelled dataset which contains 1, 930 violent videos. Each video is labelled with 6 objective violent attributes. Furthermore, we employ pairwise comparison method to obtain ground-truth violence rating for each video. Our proposed approach was evaluated on our dataset. Its results showed that our proposed method outperforms the state-of-art video classification methods.