Affiliation:
1. Peking University, Beijing, China
2. Aalborg University, Aalborg East, Denmark
3. Peking University, Shenzhen, China
Abstract
The exponential increase of online videos greatly enriches the life of users but also brings huge numbers of near-duplicate videos (NDVs) that seriously challenge the video websites. The video websites entail NDV-related applications such as detection of copyright violation, video monitoring, video re-ranking, and video recommendation. Since these applications adopt different features and different processing procedures due to diverse scenarios, constructing separate and special-purpose systems for them incurs considerable costs on design, implementation, and maintenance. In this article, we propose a general NDV system on Storm (GVoS)—a popular distributed real-time stream processing platform—to simultaneously support a wide variety of video applications. The generality of GVoS is achieved in two aspects. First, we extract the reusable components from various applications. Second, we conduct the communication between components via a mechanism called Stream Shared Message (SSM) that contains the video-related data. Furthermore, we present an algorithm to reduce the size of SSM in order to avoid the data explosion and decrease the network latency. The experimental results demonstrate that GVoS can achieve performance almost the same as the customized systems. Meanwhile, GVoS accomplishes remarkably higher systematic versatility and efficiently facilitates the development of various NDV-related applications.
Funder
Shenzhen Gov Research Project
973 program
National Natural Science Foundation of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Advance on large scale near-duplicate video retrieval;Frontiers of Computer Science;2020-01-03
2. SKCompress: compressing sparse and nonuniform gradient in distributed machine learning;The VLDB Journal;2020-01-01
3. SketchML;Proceedings of the 2018 International Conference on Management of Data;2018-05-27