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Social media video summarization using multi-Visual features and Kohnen's Self Organizing Map

机译：使用多视觉功能和Kohnen的自组织图汇总社交媒体视频

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摘要

Social networking tools such as Facebook, YouTube, Twitter, and Instagram, are becoming major platforms for communication. YouTube as one of the primary video sharing platform serves over 100 million distinct videos, 300 hours of videos are uploaded on YouTube every minute along with textual data. This massive amount of multimedia data needs to be managed with high efficiency, the irrelevant and redundant data needs to be removed. Video summarization ideals with the problem of redundant data in a video. A summarized video contains the most distinct frames which are termed as key frames. Most of the research work on key frames extraction considers only a single visual feature which is not sufficient for capturing the full pictorial details and hence affecting the quality of video summary generated. So there is a need to explore multiple visual features for key frames extraction. In this research work a key frame extraction technique based upon fusion of four visual features namely: correlation of RGB color channels, color histogram, mutual information and moments of inertia is proposed. Kohonen Self Organizing map as a clustering approach is used to find the most representative frames from the list of frames coming after fusion. Useless frames are discarded and frames having maximum Euclidean distance within a cluster are selected as final key frames. The results of the proposed technique are compared with the existing video summarization techniques: User generated summary, Video SUMMarization (VSUMM), and Video Key Frame Extraction through Dynamic Delaunay Clustering (VKEDDCSC) which shows a considerable improvement in terms of fidelity and Shot Reconstruction Degree (SRD) score.
机译：诸如Facebook，YouTube，Twitter和Instagram之类的社交网络工具正在成为主要的交流平台。 YouTube是主要的视频共享平台之一，可提供超过1亿个不同的视频，每分钟300个小时的视频以及文字数据会上传到YouTube。这些海量的多媒体数据需要进行高效管理，不相关的冗余数据也需要删除。视频摘要理想用于视频中的冗余数据问题。摘要视频包含最不同的帧，称为关键帧。关于关键帧提取的大多数研究工作仅考虑单个视觉功能，这不足以捕获完整的图像细节，因此影响生成的视频摘要的质量。因此，需要探索多种视觉特征以提取关键帧。在这项研究工作中，提出了一种基于四种视觉特征融合的关键帧提取技术：RGB颜色通道的相关性，颜色直方图，互信息和惯性矩。 Kohonen自组织图作为聚类方法，用于从融合后的帧列表中找到最具代表性的帧。丢弃无用的帧，并选择簇中具有最大欧几里得距离的帧作为最终关键帧。将该技术的结果与现有的视频摘要技术进行了比较：用户生成的摘要，视频SUMMarization（VSUMM）和通过动态Delaunay聚类（VKEDDCSC）提取视频关键帧，这在保真度和镜头重建度方面显示了相当大的改进（SRD）得分。

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