基于时空特征融合的恶意视频分类

Jaehyun Jeon, Semin Kim, S. Han, Yong Man Ro
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引用次数: 0

摘要

近年来,恶意视频的分类和过滤技术越来越受到人们的关注,因为人们可以通过互联网、IPTV、在线社交网络等方便地访问恶意多媒体内容。在开发恶意视频分类和过滤系统方面已经做了大量的研究工作。然而,恶意视频的分类过滤在可靠的分类过滤性能方面还不成熟。特别是,大多数传统方法都局限于仅使用空间特征(例如皮肤区域和视觉词袋的比例)来进行恶意图像分类。因此,以前的方法仅限于实现可接受的分类和过滤性能。为了克服上述限制,我们提出了新的恶意视频分类框架,该框架利用了从视频帧序列中容易提取的空间和时间特征。特别地,我们基于运动周期性特征和时间相关性开发了有效的时间特征。此外,为了将空间特征和时间特征结合起来,利用最佳的数据融合方法,将具有代表性的数据融合方法应用到该框架中。为了证明我们方法的有效性,我们收集了200个性交视频和200个非性交视频。实验结果表明,该方法对性交视频的分类准确率提高了3.75%(从92.25%提高到96%)。此外,基于实验结果,发现特征级融合方法(用于融合空间和时间特征)可以达到最佳的分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using the fusion of spatial and temporal features for malicious video classification
Recently, malicious video classification and filtering techniques are of practical interest as ones can easily access to malicious multimedia contents through the Internet, IPTV, online social network, and etc. Considerable research efforts have been made to developing malicious video classification and filtering systems. However, the malicious video classification and filtering is not still being from mature in terms of reliable classification/filtering performance. In particular, the most of conventional approaches have been limited to using only the spatial features (such as a ratio of skin regions and bag of visual words) for the purpose of malicious image classification. Hence, previous approaches have been restricted to achieving acceptable classification and filtering performance. In order to overcome the aforementioned limitation, we propose new malicious video classification framework that takes advantage of using both the spatial and temporal features that are readily extracted from a sequence of video frames. In particular, we develop the effective temporal features based on the motion periodicity feature and temporal correlation. In addition, to exploit the best data fusion approach aiming to combine the spatial and temporal features, the representative data fusion approaches are applied to the proposed framework. To demonstrate the effectiveness of our method, we collect 200 sexual intercourse videos and 200 non-sexual intercourse videos. Experimental results show that the proposed method increases 3.75% (from 92.25% to 96%) for classification of sexual intercourse video in terms of accuracy. Further, based on our experimental results, feature-level fusion approach (for fusing spatial and temporal features) is found to achieve the best classification accuracy.
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