Advancing video categorization

Vubble是一家媒体技术公司，为值得信赖的数字视频分发和管理构建解决方案。Vubble使用**算法和人类策展人的组合，在互联网上搜索用户感兴趣的视频内容。Vubble自成立以来**经历了显著的同比增长，教育和媒体领域的客户群**不断扩大。他们花了几年时间建立自己的技术，并确认他们的产品市场适合早期客户，**已经准备好扩大规模。通过这个项目，Vubble与Senecas信息与通信技术学院的Vida Movahedi博士合作开发了一种机器学习算法，该算法将自动为**视频输出高概率类别。通过在Vubble工作流程中实施此算法来协助自动视频识别，Vubble将能够**更好地满足他们现有的和新兴的客户需求，同时提高他们的生产力和竞争力。这个**视频识别研究项目将是Vubble了解如何自动识别准确的**视频的第一步。随着视频迅速成为世界上占主导地位的媒体消费形式，尤其是对数字原生年轻受众来说，对视频自动化管理的需求非常普遍。此外，应用研究的结果将帮助Vubble向前迈进，他们认为这是所有媒体消费者和社会面临的一个迫在眉睫的问题，即从档案片段创建的假新闻视频的兴起。

Vubble is a media tech company that builds solutions for trustworthy digital video distribution and curation. Using a combination of**algorithms and human curators, Vubble searches the internet to locate video content of interest to its users. Vubble has**experienced significant year over year growth since incorporation, with an expanding customer base in the education and media**spaces. They have spent several years building their technology and confirming their product market fit with early customers and**are ready to scale. Through this project, Vubble with collaborate with Dr. Vida Movahedi from Senecas School of Information and**Communications Technology to develop a machine-learning algorithm that will automatically output highly probable categories for**videos. With this algorithm implemented into the Vubble workflow to assist in automated video identification, Vubble will be able to**better address their existing, and emerging, customer demands, while increasing their productivity and competitiveness. This**video identification research project will be Vubbles first step in understanding how to automate the identification of accurate**video. The need for automation of videos curation is prevalent, as video is quickly becoming the worlds dominant form of media**consumption, particularly for digital-native younger audiences. Furthermore, the results of the applied research will aid Vubble in**moving forward in addressing what they believe is a looming problem facing all media consumers, and society, the rising of fake**news video created from archival footage.