Video fingerprinting based on quadruplet convolutional neural network

Abstract

In order to achieve fast and accurate retrieval of video copies, this paper proposes a compact video fingerprinting based on quadruplet convolutional neural network. The algorithm consists of four branch networks with shared weights, each branch network contains feature extraction and quantization coding. The projection and excitation network is combined with 3D convolution for feature extraction, which mainly learns feature weights to improve useful features and suppresses valueless features. The deep features learned are mapped into approximate real vectors in a fully connected form and quantized to generate binary codes. The model employs an improved quadruplet loss to divide the feature distance between the copied videos and the non-copied, and a quantization error term is added to ensure that the fingerprint codes contains as much similar information as the original data. The experimental results performed on the public dataset show that the algorithm can effectively improve the robustness and distinctiveness, and the average detection accuracy under multiple compound attacks is better than the compared algorithms.

Keywords

• video fingerprinting
• 3d convolution
• projection and excitation
• quadruplet loss