Lightweight Fisher Vector Transfer Learning for Video Deduplication

Video deduplication in cloud and on devices is a key challenge for storage and communication efficiency. The lifetime of video content creation, communication/sharing, and consumption can generate multiple versions of the same content with variations in coding and editing effects. In this work, we develop a lightweight and robust deduplication feature based on the fisher vector aggregation of Scale-Invariant Feature Transform (SIFT) keypoints. The fisher vector representation is used for a deduplication transfer learning process that utilizes a lightweight Multilayer Perceptron (MLP) network with center loss to learn a compact and distinctive feature. Simulation on the CC\_WEB\_VIDEO dataset demonstrated that the proposed feature is extremely robust in deduplication with respect to typical editing effects and coding/transcoding degenerations while being computationally very lightweight compared to other solutions.